Durbib- Watson D between 0-2 means there is a positive correlation at 92% of 1st Order Correlation(1 time unit lag).
The summary shows a linear regression of CO2 emissions vs time. The p-value(<0.05) suggests that the model is significant. The model also defines 68% of the variability in data(R-square=0.6844). The equation for our regression model will be:
CO2 emissions = 0.00002008*Date+0.11162
The residual values doesn’t seem random but normal. We can also see that for higher values of CO2 emissions (>0.5) the variance increases. So, we will check a squared model to see if that explains the data prediction better.
The squared regression shows better R square of 78% and p-values show that Date and Date^2 coefficients are significant but intercept is not. New equation will be:
CO2 emissions = 5.9E-10*(Date^2) +0.0000246*Date-0.009480
Now we will run the time series using ARIMA that includes Auto Regression and Moving Average. To run an ARIMA model we need to define p (lags in Auto Regression), d (non-seasonal difference) and q (lagged forecast errors in Moving Average). These attribute will be defined by checking for seasonality, ACF and PACF plots.
The Autocorrelation check is used to test for white noise. If the p-value is significant, we can say that the data is correlated else the data is independent. Here the tables shows the autocorrelations at different lags and p-values suggest that the data is correlated.
The ACF and PACF plots are used to identify p (lag for auto regression) and seasonality. We can see that ACF plot starts with a positive value and then continues with negative values till 12. But there is no pattern following.
So, we can say that AR is explained very well using lag-1. Also, the PACF plot cuts off at 2. We will iterate through different pdq values and get the best estimates with lowest AIC score.
The pdq (2,1,1) shows better AIC -2426 compared to other pdq values as well as squared regression. The p-value <0.05 also signifies that the parameters we have selected are good. We will predict using these parameters
The distribution of residuals are normal unlike regression and squared regression
The tables show the equation for Autoregression and Moving Average prediction of ARIMA model
This tables shows the forecast of next 12 months of data
Graphical Forecast highlighted by line at the end and connected with the existing data. So this plot shows the complete trend of historical data+predicted data
The last table shows the outliers with row number and values of the observations.
Monday, 21 June 2021 00:08:42 1
Model: MODEL1
Dependent Variable: CO2
Number of Observations Read 1680
Number of Observations Used 1680
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 0 0 . . .
Error 1679 215.11655 0.12812
Corrected Total 1679 215.11655
Root MSE 0.35794 R-Square 0.0000
Dependent Mean 0.03483 Adj R-Sq 0.0000
Coeff Var 1027.75772
Parameter Estimates
Variable D ...
InstructionsView CAAE Stormwater video Too Big for Our Ditches.docxdirkrplav
Instructions:
View CAAE Stormwater video "Too Big for Our Ditches"
http://www.ncsu.edu/wq/videos/stormwater%20video/SWvideo.html
Explain how impermeable surfaces in the urban environment impact the stream network in a river basin. Why is watershed management an important consideration in urban planning? Unload you essay (200-400 words).
Neal.LarryBUS457A7.docx
Question 1
Problem:
It is not certain about the relationship between age, Y, as a function of systolic blood pressure.
Goal:
To establish the relationship between age Y, as a function of systolic blood pressure.
Finding/Conclusion:
Based on the available data, the relationship is obtained and shown below:
Regression Analysis: Age versus SBP
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 2933 2933.1 21.33 0.000
SBP 1 2933 2933.1 21.33 0.000
Error 28 3850 137.5
Lack-of-Fit 21 2849 135.7 0.95 0.575
Pure Error 7 1002 143.1
Total 29 6783
Model Summary
S R-sq R-sq(adj) R-sq(pred)
11.7265 43.24% 41.21% 3.85%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -18.3 13.9 -1.32 0.198
SBP 0.4454 0.0964 4.62 0.000 1.00
Regression Equation
Age = -18.3 + 0.4454 SBP
It is found that there is an outlier in the dataset, which significantly affect the regression equation. As a result, the outlier is removed, and the regression analysis is run again.
Regression Analysis: Age versus SBP
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 4828.5 4828.47 66.81 0.000
SBP 1 4828.5 4828.47 66.81 0.000
Error 27 1951.4 72.27
Lack-of-Fit 20 949.9 47.49 0.33 0.975
Pure Error 7 1001.5 143.07
Total 28 6779.9
Model Summary
S R-sq R-sq(adj) R-sq(pred)
8.50139 71.22% 70.15% 66.89%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -59.9 12.9 -4.63 0.000
SBP 0.7502 0.0918 8.17 0.000 1.00
Regression Equation
Age = -59.9 + 0.7502 SBP
The p-value for the model is 0.000, which implies that the model is significant in the prediction of Age. The R-square of the model is 70.2%, implies that 70.2% of variation in age can be explained by the model
Recommendation:
The regression model Age = -59.9 +0.7502 SBP can be used to predict the Age, such that over 70% of variation in Age can be explained by the model.
Question 2
Problem:
It is not sure that whether the factors X1 to X4 which represents four different success factors have any influences on the annual savings as a result of CRM implementation.
Goal:
To determine which of the success factors are most significant in the prediction of a successful CRM program, and develop the corresponding model for the prediction of CRM savings.
Finding/Conclusion:
Based on the available da.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
A Study on the Short Run Relationship b/w Major Economic Indicators of US Eco...aurkoiitk
The objective of this study
was to develop an economic indicator system for the US
economy that will help to forecast the turning points in the
aggregate level of economic activity. Our primary concern
is to study the short run relationship between the major
economic indicators of US economy (eg: GDP, Money
Supply, Unemployment Rate, Inflation rate, Federal Fund
Rate, Exchange Rate, Government Expenditure &
Receipt, Crude Oil Price, Net Import & Export).
An introduction to SigmaXL's various Graphical tools
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
InstructionsView CAAE Stormwater video Too Big for Our Ditches.docxdirkrplav
Instructions:
View CAAE Stormwater video "Too Big for Our Ditches"
http://www.ncsu.edu/wq/videos/stormwater%20video/SWvideo.html
Explain how impermeable surfaces in the urban environment impact the stream network in a river basin. Why is watershed management an important consideration in urban planning? Unload you essay (200-400 words).
Neal.LarryBUS457A7.docx
Question 1
Problem:
It is not certain about the relationship between age, Y, as a function of systolic blood pressure.
Goal:
To establish the relationship between age Y, as a function of systolic blood pressure.
Finding/Conclusion:
Based on the available data, the relationship is obtained and shown below:
Regression Analysis: Age versus SBP
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 2933 2933.1 21.33 0.000
SBP 1 2933 2933.1 21.33 0.000
Error 28 3850 137.5
Lack-of-Fit 21 2849 135.7 0.95 0.575
Pure Error 7 1002 143.1
Total 29 6783
Model Summary
S R-sq R-sq(adj) R-sq(pred)
11.7265 43.24% 41.21% 3.85%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -18.3 13.9 -1.32 0.198
SBP 0.4454 0.0964 4.62 0.000 1.00
Regression Equation
Age = -18.3 + 0.4454 SBP
It is found that there is an outlier in the dataset, which significantly affect the regression equation. As a result, the outlier is removed, and the regression analysis is run again.
Regression Analysis: Age versus SBP
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 4828.5 4828.47 66.81 0.000
SBP 1 4828.5 4828.47 66.81 0.000
Error 27 1951.4 72.27
Lack-of-Fit 20 949.9 47.49 0.33 0.975
Pure Error 7 1001.5 143.07
Total 28 6779.9
Model Summary
S R-sq R-sq(adj) R-sq(pred)
8.50139 71.22% 70.15% 66.89%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -59.9 12.9 -4.63 0.000
SBP 0.7502 0.0918 8.17 0.000 1.00
Regression Equation
Age = -59.9 + 0.7502 SBP
The p-value for the model is 0.000, which implies that the model is significant in the prediction of Age. The R-square of the model is 70.2%, implies that 70.2% of variation in age can be explained by the model
Recommendation:
The regression model Age = -59.9 +0.7502 SBP can be used to predict the Age, such that over 70% of variation in Age can be explained by the model.
Question 2
Problem:
It is not sure that whether the factors X1 to X4 which represents four different success factors have any influences on the annual savings as a result of CRM implementation.
Goal:
To determine which of the success factors are most significant in the prediction of a successful CRM program, and develop the corresponding model for the prediction of CRM savings.
Finding/Conclusion:
Based on the available da.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
A Study on the Short Run Relationship b/w Major Economic Indicators of US Eco...aurkoiitk
The objective of this study
was to develop an economic indicator system for the US
economy that will help to forecast the turning points in the
aggregate level of economic activity. Our primary concern
is to study the short run relationship between the major
economic indicators of US economy (eg: GDP, Money
Supply, Unemployment Rate, Inflation rate, Federal Fund
Rate, Exchange Rate, Government Expenditure &
Receipt, Crude Oil Price, Net Import & Export).
An introduction to SigmaXL's various Graphical tools
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
Chapter 16 Inference for RegressionClimate ChangeThe .docxketurahhazelhurst
Chapter 16: Inference for Regression
Climate Change
The earth has been getting warmer. Most climate scientists agree that one important cause of the warming is
the increase in atmospheric levels of carbon dioxide (CO2), a green house gas. Here is part of a regression
analysis of the mean annual CO2 concentration (CO2) in the atmosphere, measured in parts per thousand
(ppt), at the top of Mauna Loa in Hawaii and the mean annual air temperature (Temp) over both land and
sea across the globe, in degrees Celsius.
Let’s first read the dataset into R
climate <- read.table('Climate_Change.txt', sep = '\t', header = TRUE)
and take a look at the data structure:
str(climate)
## 'data.frame': 29 obs. of 3 variables:
## $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ...
## $ Temp: num 14.2 14.3 14.1 14.3 14.1 ...
## $ CO2 : num 339 340 341 342 344 ...
We see three variables, which are year, Temp (mean annual air temperature) and CO2 (mean annual CO2
concentration), and there are 29 observations in each variable.
We now take Temp as the response variable and CO2 the predictor variable, to study their relationship. To see
if linear regression is appropriate, we make a scatterplot of Temp against CO2
plot(climate$CO2, climate$Temp, xlab = 'CO2 Concentration', ylab = 'Temperature')
340 350 360 370 380
1
4
.1
1
4
.3
1
4
.5
CO2 Concentration
Te
m
p
e
ra
tu
re
It seems reasonable to fit a linear model to the dataset, because both variables are quantitative, the data
points show a linear pattern, and there is no outlier. So, let’s fit the model:
imod <- lm(Temp ~ CO2, data = climate)
1
The summary of the fitted model is given by
summary(imod)
##
## Call:
## lm(formula = Temp ~ CO2, data = climate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16809 -0.07972 0.00194 0.07013 0.18532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.707076 0.481006 22.260 < 2e-16 ***
## CO2 0.010062 0.001336 7.534 4.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09847 on 27 degrees of freedom
## Multiple R-squared: 0.6776, Adjusted R-squared: 0.6657
## F-statistic: 56.76 on 1 and 27 DF, p-value: 4.192e-08
which contains a lot of information. We see that R2 = 0.6776 and the SD of residuals se = 0.09847 (the
estimator of population standard deviation σ) with 27 degrees of freedom. In Coefficients section we
see the intercept b0 = 10.71 and the slope b1 = 0.01. Their standard errors are SE(b0) = 0.481 and
SE(b1) = 0.00134. Their t-test statistics are t0 = b0/SE(b0) = 22.26 and t1 = b1/SE(b1) = 7.534. Their
corresponding (two-tailed) p-values are very small (<2e-16 and 4.19e-08). As a result, we reject H0 : β1 = 0
and conclude there is a positive correlation between Temp and CO2. The b1 = 0.01 can be interpreted as
follows: The air temperature will increase by 0.01 degrees Celsius on average if the CO2 concentration in the
atmosphere increases by 1 p ...
PID controller using rapid control prototyping techniquesIJECEIAES
To analyze the performance of the PID controller in a buck type converter implemented in real time. We begin by designing a continuous controller using the analytical method for calculating PIDs. Pulse width modulation is then used and bifurcation diagrams analyzed to reveal some problems of switching and sampling time. The model converter is then implemented with a PID controller in dSPACE. The experimental results provide detailed requirements of sampling frequency and switching speed, and show the performance of the PID controller. Converters are used in power generation solar systems and conmuted power sources for feed telecommunication devices, smart grids, and other applications.
Optimization of performance and emission characteristics of dual flow diesel ...eSAT Journals
Abstract
Depleting sources of fossil fuels coupled with after effects of exhaust gases on environment i.e. global warming and climate change has necessitated the need for development and use of alternate biodegradable fuels. In this present study optimization of performance and emission characteristics has been carried out using dual flow of CNG and Diesel with varying EGR under varying load by Taguchi method. Optimum values of output response parameters have been calculated with the help of regression equation and influence of various factors on output response has carried out with the help of analysis of variance.
Keywords: Taguchi method, CNG, EGR, biodegradable fuels
Ch 03 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 3 of the book entitled "MATLAB Applications in Chemical Engineering": Interpolation, Differentiation, and Integration. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
OPTIMAL PID CONTROLLER DESIGN FOR SPEED CONTROL OF A SEPARATELY EXCITED DC MO...ijscmcj
This paper presents a new approach to determine the optimal proportional-integral-derivative controller
parameters for the speed control of a separately excited DC motor using firefly optimization technique.
Firefly algorithm is one of the recent evolutionary methods which are inspired by the Firefly’s behavior in
nature. The firefly optimization technique is successfully implemented using MATLAB software. A
comparison is drawn from the results obtained between the linear quadratic regulator and firefly
optimization techniques. Simulation results are presented to illustrate the performance and validity of the
design method.
12-15 page paper with 5 slide PowerPoint on an current management .docxAlyciaGold776
12-15 page paper with 5 slide PowerPoint on an current management issue, MY ISSUES:
(OPEN COMMUNICATION)
PAPER WILL BE SUBMITTED TO TURNIN!
APA, (6
TH
ED.) paper has to included title page& table of content
No pronouns
Cite all quantitative data
Cite all quotes ( try not to use quotations)
Intro ½ page
Background ¾ page
Literature review 4-5 page
Analysis 3-4 page ( detailed information)
Conclusion ¾ page ( WHAT,WHY,HOW,WHOM)
References page-Minimum of 20 published scholarly sources current as possible
Abstract (these questions has to be answered)
Clear statement of problem or issue
Methods or procedures summarized
Results summarized
Conclusions summarized
DUES BY MAY28 5PM
.
12Working With FamiliesThe Case of Carol and JosephCa.docxAlyciaGold776
12
Working With Families:
The Case of Carol and Joseph
Carol is a 23-year-old, heterosexual, Caucasian female and the
mother of a 1-year-old baby girl. She is currently unemployed,
having previously worked for a house cleaning company. The
baby is healthy and developmentally on target, and she and the
parents appear to be well bonded with one another. Carol lives in
a rented house with her husband, Joseph. Joseph is a 27-year-old,
heterosexual, Hispanic male. He was recently arrested at their
home for a drug deal, which he asserts was a setup. Both parents
were charged with child endangerment because weapons were
found in the child’s crib and drugs were found in the home. The
parents assert that the child never sleeps in the crib but in their
bed. As a result of the parents’ arrest, social services was notified,
and the child was temporarily placed in a kinship care arrangement
with the maternal grandmother, who resides nearby. As a
result of Joseph’s arrest, he was fired from the cleaning company
where he worked, and the family is now experiencing financial
difficulties.
After initial contact was made with the parents, a number of
concerns were noted and the family was recommended for additional
case management. Carol’s mother indicated that she had
concerns about Carol’s drinking habits and stated that Carol’s
father and grandfather were alcoholics. She and the father separated
when Carol was a baby, and Carol has had only limited
contact with him. There appears to be significant tension between
the grandmother and Carol and Joseph. I addressed the alcohol
issue with both parents, who denied there was a problem, but
shortly after the discussion, Carol was involved in a serious car
accident with the baby in the car. She was determined to have been
under the influence of alcohol. I advised Carol that she could not
have any unsupervised contact with her child until she completed
intensive inpatient substance abuse treatment. I made arrangements
for her placement, but after a week, she was discharged
for noncompliance with the rules. She was then referred to an
intensive outpatient program and began therapy there. Initially
her attendance was erratic because she had lost her license as a
result of the DUI. Eventually, however, she became engaged in the
program and began to address her issues. She acknowledged that
she had started using drugs at a very young age but said that she
had only begun drinking in the previous year or so. We discussed
the genetics of her family, and she said that she realized that she
had deteriorated rapidly since beginning to drink and knew that
she simply could not drink alcohol.
Joseph’s mother is deceased, and his father travels extensively
in his job and is not available as a support. Joseph was
very devoted to his mother and was devastated by her premature
death. We discussed the strengths that he and Carol demonstrated
in staying together and working out their p.
More Related Content
Similar to Durbib- Watson D between 0-2 means there is a positive correlati
Chapter 16 Inference for RegressionClimate ChangeThe .docxketurahhazelhurst
Chapter 16: Inference for Regression
Climate Change
The earth has been getting warmer. Most climate scientists agree that one important cause of the warming is
the increase in atmospheric levels of carbon dioxide (CO2), a green house gas. Here is part of a regression
analysis of the mean annual CO2 concentration (CO2) in the atmosphere, measured in parts per thousand
(ppt), at the top of Mauna Loa in Hawaii and the mean annual air temperature (Temp) over both land and
sea across the globe, in degrees Celsius.
Let’s first read the dataset into R
climate <- read.table('Climate_Change.txt', sep = '\t', header = TRUE)
and take a look at the data structure:
str(climate)
## 'data.frame': 29 obs. of 3 variables:
## $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ...
## $ Temp: num 14.2 14.3 14.1 14.3 14.1 ...
## $ CO2 : num 339 340 341 342 344 ...
We see three variables, which are year, Temp (mean annual air temperature) and CO2 (mean annual CO2
concentration), and there are 29 observations in each variable.
We now take Temp as the response variable and CO2 the predictor variable, to study their relationship. To see
if linear regression is appropriate, we make a scatterplot of Temp against CO2
plot(climate$CO2, climate$Temp, xlab = 'CO2 Concentration', ylab = 'Temperature')
340 350 360 370 380
1
4
.1
1
4
.3
1
4
.5
CO2 Concentration
Te
m
p
e
ra
tu
re
It seems reasonable to fit a linear model to the dataset, because both variables are quantitative, the data
points show a linear pattern, and there is no outlier. So, let’s fit the model:
imod <- lm(Temp ~ CO2, data = climate)
1
The summary of the fitted model is given by
summary(imod)
##
## Call:
## lm(formula = Temp ~ CO2, data = climate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16809 -0.07972 0.00194 0.07013 0.18532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.707076 0.481006 22.260 < 2e-16 ***
## CO2 0.010062 0.001336 7.534 4.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09847 on 27 degrees of freedom
## Multiple R-squared: 0.6776, Adjusted R-squared: 0.6657
## F-statistic: 56.76 on 1 and 27 DF, p-value: 4.192e-08
which contains a lot of information. We see that R2 = 0.6776 and the SD of residuals se = 0.09847 (the
estimator of population standard deviation σ) with 27 degrees of freedom. In Coefficients section we
see the intercept b0 = 10.71 and the slope b1 = 0.01. Their standard errors are SE(b0) = 0.481 and
SE(b1) = 0.00134. Their t-test statistics are t0 = b0/SE(b0) = 22.26 and t1 = b1/SE(b1) = 7.534. Their
corresponding (two-tailed) p-values are very small (<2e-16 and 4.19e-08). As a result, we reject H0 : β1 = 0
and conclude there is a positive correlation between Temp and CO2. The b1 = 0.01 can be interpreted as
follows: The air temperature will increase by 0.01 degrees Celsius on average if the CO2 concentration in the
atmosphere increases by 1 p ...
PID controller using rapid control prototyping techniquesIJECEIAES
To analyze the performance of the PID controller in a buck type converter implemented in real time. We begin by designing a continuous controller using the analytical method for calculating PIDs. Pulse width modulation is then used and bifurcation diagrams analyzed to reveal some problems of switching and sampling time. The model converter is then implemented with a PID controller in dSPACE. The experimental results provide detailed requirements of sampling frequency and switching speed, and show the performance of the PID controller. Converters are used in power generation solar systems and conmuted power sources for feed telecommunication devices, smart grids, and other applications.
Optimization of performance and emission characteristics of dual flow diesel ...eSAT Journals
Abstract
Depleting sources of fossil fuels coupled with after effects of exhaust gases on environment i.e. global warming and climate change has necessitated the need for development and use of alternate biodegradable fuels. In this present study optimization of performance and emission characteristics has been carried out using dual flow of CNG and Diesel with varying EGR under varying load by Taguchi method. Optimum values of output response parameters have been calculated with the help of regression equation and influence of various factors on output response has carried out with the help of analysis of variance.
Keywords: Taguchi method, CNG, EGR, biodegradable fuels
Ch 03 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 3 of the book entitled "MATLAB Applications in Chemical Engineering": Interpolation, Differentiation, and Integration. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
OPTIMAL PID CONTROLLER DESIGN FOR SPEED CONTROL OF A SEPARATELY EXCITED DC MO...ijscmcj
This paper presents a new approach to determine the optimal proportional-integral-derivative controller
parameters for the speed control of a separately excited DC motor using firefly optimization technique.
Firefly algorithm is one of the recent evolutionary methods which are inspired by the Firefly’s behavior in
nature. The firefly optimization technique is successfully implemented using MATLAB software. A
comparison is drawn from the results obtained between the linear quadratic regulator and firefly
optimization techniques. Simulation results are presented to illustrate the performance and validity of the
design method.
12-15 page paper with 5 slide PowerPoint on an current management .docxAlyciaGold776
12-15 page paper with 5 slide PowerPoint on an current management issue, MY ISSUES:
(OPEN COMMUNICATION)
PAPER WILL BE SUBMITTED TO TURNIN!
APA, (6
TH
ED.) paper has to included title page& table of content
No pronouns
Cite all quantitative data
Cite all quotes ( try not to use quotations)
Intro ½ page
Background ¾ page
Literature review 4-5 page
Analysis 3-4 page ( detailed information)
Conclusion ¾ page ( WHAT,WHY,HOW,WHOM)
References page-Minimum of 20 published scholarly sources current as possible
Abstract (these questions has to be answered)
Clear statement of problem or issue
Methods or procedures summarized
Results summarized
Conclusions summarized
DUES BY MAY28 5PM
.
12Working With FamiliesThe Case of Carol and JosephCa.docxAlyciaGold776
12
Working With Families:
The Case of Carol and Joseph
Carol is a 23-year-old, heterosexual, Caucasian female and the
mother of a 1-year-old baby girl. She is currently unemployed,
having previously worked for a house cleaning company. The
baby is healthy and developmentally on target, and she and the
parents appear to be well bonded with one another. Carol lives in
a rented house with her husband, Joseph. Joseph is a 27-year-old,
heterosexual, Hispanic male. He was recently arrested at their
home for a drug deal, which he asserts was a setup. Both parents
were charged with child endangerment because weapons were
found in the child’s crib and drugs were found in the home. The
parents assert that the child never sleeps in the crib but in their
bed. As a result of the parents’ arrest, social services was notified,
and the child was temporarily placed in a kinship care arrangement
with the maternal grandmother, who resides nearby. As a
result of Joseph’s arrest, he was fired from the cleaning company
where he worked, and the family is now experiencing financial
difficulties.
After initial contact was made with the parents, a number of
concerns were noted and the family was recommended for additional
case management. Carol’s mother indicated that she had
concerns about Carol’s drinking habits and stated that Carol’s
father and grandfather were alcoholics. She and the father separated
when Carol was a baby, and Carol has had only limited
contact with him. There appears to be significant tension between
the grandmother and Carol and Joseph. I addressed the alcohol
issue with both parents, who denied there was a problem, but
shortly after the discussion, Carol was involved in a serious car
accident with the baby in the car. She was determined to have been
under the influence of alcohol. I advised Carol that she could not
have any unsupervised contact with her child until she completed
intensive inpatient substance abuse treatment. I made arrangements
for her placement, but after a week, she was discharged
for noncompliance with the rules. She was then referred to an
intensive outpatient program and began therapy there. Initially
her attendance was erratic because she had lost her license as a
result of the DUI. Eventually, however, she became engaged in the
program and began to address her issues. She acknowledged that
she had started using drugs at a very young age but said that she
had only begun drinking in the previous year or so. We discussed
the genetics of her family, and she said that she realized that she
had deteriorated rapidly since beginning to drink and knew that
she simply could not drink alcohol.
Joseph’s mother is deceased, and his father travels extensively
in his job and is not available as a support. Joseph was
very devoted to his mother and was devastated by her premature
death. We discussed the strengths that he and Carol demonstrated
in staying together and working out their p.
12 pages The papers must be typed (12 point font) in Times N.docxAlyciaGold776
1
2
pages
The papers must be typed (12 point font) in Times New Roman Font; double-spaced (unless otherwise noted), with one inch margins.
the organization should be a business or company basis.
Provide the links for the company's news.
You show up for work in a new organization or “parachute” into the organization (often knowing little about the organization).
This analytical paper describes how you observe and orient in this new environment to more fully understand the organization’s behavior.
address the deeper
currents of culture
as well as how
processes
and
mission
drive behavior.
address your place in this organization (from which perspective are you writing), not merely from a “job description” perspective, but from at the individual and group levels of analysis.
While not limited to these topics
address leadership, motivation, communications, and ethics.
In addition to incorporating
a wide variety of specific OB distinctions from the course
, the paper must
analyze
(not merely describe) the organization’s behavior from each zoom level:
individual, group, organization, and inter-organizational.
t
h
ird-person perspective
to analyze the organization.
The paper requires you to think deeply about OB in a specific organization.
use an organization you have experience with or research one where you would like to work.
Ideally, from reading this paper, professor should have the experience of being there with you and gain a valuable understanding of this organization.
Another way to look at this paper is as the document which uncovers the currents of organizational behavior in a methodical way.
The exercise of writing this paper provides you with a template for analyzing your next organization’s behavior, to avoid organizational pitfalls, and more quickly make a valuable contribution.
Organizational behavior concepts include: (analyze at least 8 concepts below from
individual, group, organization, and inter-organizational as well as the culture perspective
)
Diversity
Attitudes and Job Satisfaction
Emotions and Moods
Personality and Values
Perception and Individual Decision Making
Motivation Concepts and Application
Foundations of Group Behavior & Understanding Work Teams
Communication
Leadership
Inter-Organizational Behavior
Power and Politics
Conflict and Negotiation
Foundations of Organizational Structure
Organizational Culture
Organizational Change and Stress Management
.
12 new times roman 4-6 pages double spaced apply ONE of t.docxAlyciaGold776
12 new times roman
4-6 pages
double spaced
apply ONE of the theories listed below to
The Jack-Roller: A Delinquent Boys Own Story
by Clifford R. Shaw book.
Then make prediction on what happened to Stanley (protagonist of the book) BASED on the theory chosen.
Follow the guidelines CAREFULLY
Theories to choose from
·
Gottfredson and Hirschi: Self-Control Theory
·
Sampson and Laub: Age-graded Theory of Informal Social Control
·
Moffitt: Developmental Taxonomy
.
112016 @1000 a.m. 100 percent original 400-600 words with at leas.docxAlyciaGold776
11/20/16 @10:00 a.m. 100 percent original 400-600 words with at least 2 references APA format
To further support the acquisition of a new electronic health record (EHR) system, the chief information officer (CIO) has asked you, as an information technology (IT) manager, to meet with the nursing department heads to summarize the differences and the application of relational and object-oriented databases within an EHR system.
.
10–12 slides (not incl. title or ref slides) with speakers notes.docxAlyciaGold776
10–12 slides (not incl. title or ref slides) with speaker's notes
In learning about energy sources and non-fossil fuel sources, multiple technological advances were identified. These can reduce people's footprint on the planet and reduce the burden on fossil fuels.
Using already existing technology, describe ways in which people could reduce the need for external electrical and heat energy.
In completing this, you should be able to create a house that does not rely on public utilities.
Think of houses that exist in remote areas, where these public services do not reach; how can this be accomplished?
Be sure to include primary sources as well as ensure that your references are documented on the slides as they are being used. It is critical that your presentation tells a story, and is not prescribed by the prompts listed above.
.
11.1 - write a servlet that uses doGet to return a markup document t.docxAlyciaGold776
11.1 - write a servlet that uses doGet to return a markup document that provides your name, electronic mail address, and mailing address, along with a brief autobiography. test your servlet with a simple markup document.
11.2 write a servlet that returns a randomly chosen greeting from a list of five different greeting. The greetings must be stored as constant strings in the program.
.
10–15 slides with 150–200 words in the notes page.Using all 3 .docxAlyciaGold776
10–15 slides with 150–200 words in the notes page.
Using all
3 Financial Statements
(See attachment) please provide an analysis on Apix’s
assets, liabilities, cash, and profit
. As well, choose
2 additional components
on each of the sheets, and provide your initial impression on the company financial situation.
Need done by Monday morning.
Thanks Friend
.
12-20 slides needed for the business plan report provided. (SEE ATT.docxAlyciaGold776
12-20 slides needed for the business plan report provided. (SEE ATTACHED FILE) This is a new bar called Wonderland, presentation needs to be eye capturing and intriguing to make people want to buy in to the idea to make a reality.
Format
Powerpoint presentation
APA
Reference slides needed
SECOND ATTACHED FILE (PPT PRESENTATION) SHOWS HOW I STARTED IT
I posted wrong file
.
1000+ word essay MLA styleTopic Judging others is human nature..docxAlyciaGold776
1000+ word essay MLA style
Topic
: Judging others is human nature. Some of us may practice fighting the urge to be judgmental more than others, but it is a very active battle. What lessons can you argue the characters from “ A Good Man Is Hard To Find by Flannery Oconnor” and “Young Goodman Brown by Nathaniel Hawthorne” teach readers regarding the dangers of being judgmental?
Please use these strategy questions as the professor is looking for them to be addressed in the writing.
Do you have a lead-in to “hook” your reader? (an example, anecdote, scenario, startling statistic, or provocative question.)
How much background is required to properly acquaint readers with your issue?
Will your claim be placed early (introduction) or delayed (conclusion) in your paper?
What is your supporting evidence?
Have you located authoritative (expert) sources that add credibility to your argument?
Have you considered addressing opposing viewpoints?
Are you willing to make some concessions (compromises) toward opposing sides?
What type of tone (serious, comical, sarcastic, inquisitive) best relates your message to reach your audience?
One written, have you maintained a third person voice? (no “I” or “you” statements)
How will you conclude in a meaningful way? (call your readers to take action, explain why the topic has a global importance, or offer a common ground compromise that benefits all sides?)
I wanted to make the instructions clear so I am not penalized when it comes to grading.
All paragraphs should have a topic sentence and supporting sentences explaining one idea and not multiple ideas.
Things I got hit on, on past papers on here.
Intro
Opposition
Supporting argument
Conclusion
Works cited page
looking for an A+
also have a 2000 word research paper coming up soon that i'm willinng to pay good for will be posting soon
.
1000 - 1500 words in APA format. Draft Final PlanYou work for a p.docxAlyciaGold776
1000 - 1500 words in APA format. Draft /Final Plan
You work for a popular consumer electronics company that sells products such as cell phones, tablets, and personal computers. The vice president of operations has talked to you about setting up a warehousing and distribution process that can support business expansions globally. He has asked you to develop a recommendation that will help build a business plan. You need to focus on the areas of transportation regulations and policies, transportation methodologies, warehousing, distribution, and inventory management.
The company is looking to start its global expansion in the European Union and China. You will focus your analysis and recommendations for this report on importing goods into those areas from the United States and fulfilling customer orders from in-region warehousing or distribution centers. Your outline should include the following:
Part I:
Transportation Regulations and Policies
Define the goal
Explain the relevance
National security
Public safety
Environment
Unrestrained competition
Part II:
Transportation Methodologies
Economic viability
Practical use
Applications in domestic and global markets
Part III:
Warehousing and Distribution
Principles
Design
Storage and handling
Information systems and information technology
Third-party logistics providers (3PL)
Part IV:
Inventory Management
Inventory functions for intermediate and final products
Packaging techniques
.
1000 words an 5 referencesResource Blossoms Up! Case Study .docxAlyciaGold776
1000 words an 5 references
Resource
: Blossoms Up! Case Study and Email No. 3
Numerous emails have been sitting in the HR Director's in-box for two months. Smith is highly agitated that none of his have been responded to. Now that you are hired, he has asked you to address the emails immediately.
Read
Email No. 3
concerning a report needed to respond to Smith's direction that the company have its own retirement plan such as a 401(k) plan, the laws affecting such plans, and what to do about funding it since the company is in a cost-cutting mode.
Complete
Smith's directions and the instructions in the email.
Use
headings to appropriately signal the topics and keep your document organized.
Use
a minimum of five in-text citation sources within your paper and identify them in your APA correctly formatted References page.
Click
the Assignment Files tab to submit your assignment.
.
1000+ word essay MLA styleTopic While Abraham Lincoln and John .docxAlyciaGold776
1000+ word essay MLA style
Topic:
While Abraham Lincoln and John F. Kennedy were superior national leaders, everyday persons also take on the responsibilities and risks of leadership, as illustrated by Robert, The blind man, in Raymond Carver’s “Cathedral”. On the other hand, Lieutenant Jimmy Cross in Tim O’Brien’s “the things they carried” believes he has neglected his duties as the leader of his platoon.
If you were conducting a leadership workshop for your college or local community, how could you use these four individuals to illustrate key points of your presentation? What other examples—contemporary or historical, fictional or factual—might you use to illustrate leadership qualities?
In doing so, consider the Core Value of Integrity emphasized in this course. This assignment asks you to address qualities of leadership. What is the relationship between integrity and leadership? Please include in your writing your own definition of Integrity and whether those in leadership roles are assumed to have (or demonstrate) integrity.
Please use these strategy questions as the professor is looking for them to be addressed in the writing.
Do you have a lead-in to “hook” your reader? (an example, anecdote, scenario, startling statistic, or provocative question.)
How much background is required to properly acquaint readers with your issue?
Will your claim be placed early (introduction) or delayed (conclusion) in your paper?
What is your supporting evidence?
Have you located authoritative (expert) sources that add credibility to your argument?
Have you considered addressing opposing viewpoints?
Are you willing to make some concessions (compromises) toward opposing sides?
What type of tone (serious, comical, sarcastic, inquisitive) best relates your message to reach your audience?
One written, have you maintained a third person voice? (no “I” or “you” statements)
How will you conclude in a meaningful way? (call your readers to take action, explain why the topic has a global importance, or offer a common ground compromise that benefits all sides?)
I wanted to make the instructions clear so I am not penalized when it comes to grading.
All paragraphs should have a topic sentence and supporting sentences explaining one idea and not multiple ideas.
Things I got hit on, on past papers on here.
Intro
Opposition
Supporting argument
Conclusion
Works cited page
.
1000 words and dont use the InternetFrom the book answer the qu.docxAlyciaGold776
1000 words and don't use the Internet
From the book answer the questions
A. Did any of these authors have followed historical methods of Said's book ( Orientalism) or subaltern historians? Please give an example to prove your argument.
B. How do these histories of non-westren women contribute to non-westten historiograph?
.
100 original 0 plagiarism, with introduction and conclusion.I.docxAlyciaGold776
100% original 0 plagiarism, with introduction and conclusion.
I need no more late than tomorrow Jun 6 at 7 pm.
Middle Childhood and Adolescence Paper
(
Addresses the issue in the Population of Puerto Rico. Discusses the cultural aspects that influence.)
Prepare a 950 word paper in which you describe changes that occur during middle childhood and adolescence concerning family and peer relationships, and how they might influence future development. Be sure to include the following items in your description:
Evaluate the effect of functional and dysfunctional family dynamics on development (e.g., family structure, function, and shared and nonshared environments).
Determine the positive and negative impact of peers and changes in peer relations from middle childhood to adolescence.
Examine additional pressures faced in adolescence compared to middle childhood.
Discuss the development of moral values from middle childhood into adolescence.
Use a minimum of two peer-reviewed sources.
Format your paper consistent with APA guidelines.
.
100 Original Work.Graduate Level Writing Required.DUE .docxAlyciaGold776
100% Original Work.
Graduate Level Writing Required.
DUE: Sunday, June 12, 2020 by 5pm Eastern Standard Time.
Background:
Views on justice impact many areas of criminal justice, including the concepts of fairness, equality, and impartiality, and influence the ethical standards you apply in various situations in the field. Your views on justice and how you act in situations will affect the opinions others have of you in the communities you serve. Views on justice also impact actions taken and decisions made that affect the wider population.
Write
a 1,150- to 1,400-word paper describing the origins of the concept of justice and how you believe they are defined today.
Include the following:
-Explain Aristotle’s ethical ideas of distributive and procedural justice.
-Compare substantive justice and procedural justice, including how procedural justice impacts wrongful convictions and moral perceptions of racial discrimination, such as the Central Park Five and the story of Brian Banks, a former football star.
-Explain how you understand justice as defined by today’s modern criminal justice agencies. Include reasoning and examples in your explanation to support your opinion.
Include at least four additional scholarly reference.
Format your paper consistent with APA guidelines
.
Must Be Graduate Level Writing
100% Original Work
.
10-1 Discussion Typical vs. Atypical DevelopmentThroughout this c.docxAlyciaGold776
10-1 Discussion: Typical vs. Atypical Development
Throughout this course, we have explored different aspects of development, and research has presented a variety of influences in the form of biological, social, emotional, and cognitive domains. At the end of nearly every chapter reading, a holistic position began to emerge that acknowledges the contribution by each domain. In our final discussion, reflect on whether a holistic approach is just as effective for accounting for atypical development as it is for typical development. Utilize examples from the course to support your position, or consider using an issue of atypical development to provide context (e.g., autism or antisocial behavior).
*******JUST NEEDS TO BE 2 TO 3 PARAGREAPHS WITH REFERENCES**********
.
100 words only 1 APA REFERENCEThe traditional approach for ide.docxAlyciaGold776
100 words only 1 APA REFERENCE
The traditional approach for identifying qualified applicants is often driven by old traditions like looking at resumes, degree, years of experience, and even looks. What other, more quantifiable measures might be used when hiring a new employee? Be specific.
.
100 Words minimumDiscussion TopicWhat is the difference betwe.docxAlyciaGold776
100 Words minimum
Discussion Topic:
What is the difference between “community intervention” and “intervention in the community”? How can health advocates thoroughly address each in, for example, public policymaking of one of the following (choose one and discuss or choose a health concern of your own liking):
Youth violence
Asthma in children
Walking track and other public access to exercise/fitness
Heart disease
Lack of availability of health food (in stores, restaurants, etc.)
.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Chapter 3 - Islamic Banking Products and Services.pptx
Durbib- Watson D between 0-2 means there is a positive correlati
1. Durbib- Watson D between 0-2 means there is a positive
correlation at 92% of 1st Order Correlation(1 time unit lag).
The summary shows a linear regression of CO2 emissions vs
time. The p-value(<0.05) suggests that the model is significant.
The model also defines 68% of the variability in data(R-
square=0.6844). The equation for our regression model will be:
CO2 emissions = 0.00002008*Date+0.11162
The residual values doesn’t seem random but normal. We can
also see that for higher values of CO2 emissions (>0.5) the
variance increases. So, we will check a squared model to see if
that explains the data prediction better.
The squared regression shows better R square of 78% and p-
values show that Date and Date^2 coefficients are significant
but intercept is not. New equation will be:
CO2 emissions = 5.9E-10*(Date^2) +0.0000246*Date-0.009480
Now we will run the time series using ARIMA that includes
Auto Regression and Moving Average. To run an ARIMA model
we need to define p (lags in Auto Regression), d (non-seasonal
difference) and q (lagged forecast errors in Moving Average).
These attribute will be defined by checking for seasonality,
ACF and PACF plots.
The Autocorrelation check is used to test for white noise. If the
p-value is significant, we can say that the data is correlated else
the data is independent. Here the tables shows the
autocorrelations at different lags and p-values suggest that the
data is correlated.
2. The ACF and PACF plots are used to identify p (lag for auto
regression) and seasonality. We can see that ACF plot starts
with a positive value and then continues with negative values
till 12. But there is no pattern following.
So, we can say that AR is explained very well using lag-1. Also,
the PACF plot cuts off at 2. We will iterate through different
pdq values and get the best estimates with lowest AIC score.
The pdq (2,1,1) shows better AIC -2426 compared to other pdq
values as well as squared regression. The p-value <0.05 also
signifies that the parameters we have selected are good. We will
predict using these parameters
The distribution of residuals are normal unlike regression and
squared regression
The tables show the equation for Autoregression and Moving
Average prediction of ARIMA model
This tables shows the forecast of next 12 months of data
Graphical Forecast highlighted by line at the end and connected
with the existing data. So this plot shows the complete trend of
historical data+predicted data
The last table shows the outliers with row number and values of
the observations.
3. Monday, 21 June 2021 00:08:42 1
Model: MODEL1
Dependent Variable: CO2
Number of Observations Read 1680
Number of Observations Used 1680
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 0 0 . . .
Error 1679 215.11655 0.12812
Corrected Total 1679 215.11655
Root MSE 0.35794 R-Square 0.0000
Dependent Mean 0.03483 Adj R-Sq 0.0000
Coeff Var 1027.75772
4. Parameter Estimates
Variable DF
Parameter
Estimate
Standard
Error t Value Pr > |t|
Intercept 1 0.03483 0.00873 3.99 <.0001
Monday, 21 June 2021 00:08:42 2
Model: MODEL1
Dependent Variable: CO2
Durbin-Watson D 0.121
Number of Observations 1680
1st Order Autocorrelation 0.939
Monday, 21 June 2021 00:08:42 3
Model: MODEL1
Dependent Variable: CO2
Fit Diagnostics for CO2
0Adj R-Square
0R-Square
10. Monday, 21 June 2021 00:08:42 4
Model: MODEL1
Dependent Variable: CO2
Residual by Regressors for CO2
Monday, 21 June 2021 00:08:42 5
Model: MODEL1
Dependent Variable: CO2
Number of Observations Read 1680
Number of Observations Used 1680
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 1 147.23077 147.23077 3639.25 <.0001
Error 1678 67.88578 0.04046
Corrected Total 1679 215.11655
11. Root MSE 0.20114 R-Square 0.6844
Dependent Mean 0.03483 Adj R-Sq 0.6842
Coeff Var 577.52741
Parameter Estimates
Variable DF
Parameter
Estimate
Standard
Error t Value Pr > |t|
Intercept 1 0.11162 0.00507 22.02 <.0001
Date 1 0.00002006 3.324518E-7 60.33 <.0001
Monday, 21 June 2021 00:08:42 6
Model: MODEL1
Dependent Variable: CO2
-0.5 0.0 0.5 1.0 1.5
Predicted Value
-0.5
0.0
0.5
12. 1.0
1.5
C
O
2
Observed by Predicted for CO2
Monday, 21 June 2021 00:08:42 7
Model: MODEL1
Dependent Variable: CO2
Fit Diagnostics for CO2
0.6842Adj R-Square
0.6844R-Square
0.0405MSE
1678Error DF
2Parameters
1680Observations
Proportion Less
0.0 0.4 0.8
Residual
0.0 0.4 0.8
16. tu
d
e
n
t
-0.4 -0.2 0.0 0.2 0.4 0.6
Predicted Value
-2
0
2
4
R
S
tu
d
e
n
t
-0.4 0.0 0.4
Predicted Value
-0.5
17. 0.0
0.5
R
e
s
id
u
a
l
Monday, 21 June 2021 00:08:42 8
Model: MODEL1
Dependent Variable: CO2
1880 1900 1920 1940 1960 1980 2000 2020
Date
-0.5
0.0
0.5
R
e
s
18. id
u
a
l
Residuals for CO2
1880 1900 1920 1940 1960 1980 2000 2020
Date
-1.0
-0.5
0.0
0.5
1.0
1.5
C
O
2
95% Prediction Limits95% Confidence LimitsFit
0.6842Adj R-Square
0.6844R-Square
0.0405MSE
1678Error DF
19. 2Parameters
1680Observations
Fit Plot for CO2
Monday, 21 June 2021 00:08:42 9
Data Set WORK.IMPORT
Dependent Variable CO2
Selection Method None
Number of Observations Read 1680
Number of Observations Used 240
Dimensions
Number of Effects 3
Number of Parameters 3
Monday, 21 June 2021 00:08:42 10
Least Squares Summary
Step
Effect
Entered
20. Number
Effects In SBC
0 Intercept 1 -920.4461
1 Date 2 -915.1941
2 Date*Date 3 -932.4611*
* Optimal Value of Criterion
Monday, 21 June 2021 00:08:42 11
Least Squares Model (No Selection)
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 2 0.46253 0.23126 11.91 <.0001
Error 237 4.60377 0.01943
Corrected Total 239 5.06630
Root MSE 0.13937
Dependent Mean -0.23004
21. R-Square 0.0913
Adj R-Sq 0.0836
AIC -700.90306
AICC -700.73285
SBC -932.46115
Parameter Estimates
Parameter DF Estimate
Standard
Error t Value Pr > |t|
Intercept 1 6.853205 1.474503 4.65 <.0001
Date 1 0.000560 0.000116 4.83 <.0001
Date*Date 1 1.0981066E-8 2.262221E-9 4.85 <.0001
Monday, 21 June 2021 00:08:42 12
Input Data Set
Name WORK.IMPORT3
Label
Time ID Variable Date
22. Time Interval MONTH
Length of Seasonal Cycle 12
Monday, 21 June 2021 00:08:42 13
Name of Variable = CO2
Period(s) of Differencing 1
Mean of Working Series 0.000173
Standard Deviation 0.124288
Number of Observations 1679
Observation(s) eliminated by differencing 1
Autocorrelation Check for White Noise
To
Lag Chi-Square DF Pr > ChiSq Autocorrelations
6 194.30 6 <.0001 -0.332 -0.031 -0.057 0.015 -0.027 -0.002
12 200.69 12 <.0001 -0.005 0.004 -0.024 -0.004 -0.022 0.052
18 214.08 18 <.0001 -0.000 0.036 -0.073 -0.004 0.010 -0.034
24 243.53 24 <.0001 0.034 -0.042 0.015 -0.015 -0.028 0.114
Trend and Correlation Analysis for CO2(1)
0 5 10 15 20 25
24. F
0 5 10 15 20 25
Lag
-1.0
-0.5
0.0
0.5
1.0
A
C
F
-0.75
-0.50
-0.25
0.00
0.25
0.50
C
O
25. 2
(1
)
0 500 1000 1500
Observation
Warning: Estimates did not improve after a ridge was
encountered in the objective function. The iteration process has
been terminated.
Warning: Estimates may not have converged.
ARIMA Estimation Optimization Summary
Estimation Method Maximum Likelihood
Parameters Estimated 4
Termination Criteria Maximum Relative Change in Estimates
Iteration Stopping Value 0.001
Criteria Value 1.77E-16
Monday, 21 June 2021 00:08:42 14
ARIMA Estimation Optimization Summary
Maximum Absolute Value of Gradient 0.028951
R-Square Change from Last Iteration 0.003419
26. Objective Function Log Gaussian Likelihood
Objective Function Value 1217.043
Marquardt's Lambda Coefficient 1E12
Numerical Derivative Perturbation Delta 0.001
Iterations 34
Warning Message Estimates may not have converged.
Maximum Likelihood Estimation
Parameter Estimate
Standard
Error t Value
Approx
Pr > |t| Lag
MU 0.0001572 0.0021544 0.07 0.9418 0
MA1,1 -0.98214 0.03823 -25.69 <.0001 1
AR1,1 -1.31265 0.04580 -28.66 <.0001 1
AR1,2 -0.32245 0.02876 -11.21 <.0001 2
Constant Estimate 0.000414
Variance Estimate 0.01377
Std Error Estimate 0.117344
28. Monday, 21 June 2021 00:08:42 15
Autocorrelation Check of Residuals
To
Lag Chi-Square DF Pr > ChiSq Autocorrelations
42 145.29 39 <.0001 0.019 0.028 -0.057 0.009 0.025 -0.020
48 155.80 45 <.0001 -0.003 0.009 0.015 -0.048 -0.036 0.046
Residual Correlation Diagnostics for CO2(1)
0 5 10 15 20 25
Lag
1.0
.05
.001
W
h
ite
N
o
is
e
P
29. ro
b
0 5 10 15 20 25
Lag
-1.0
-0.5
0.0
0.5
1.0
IA
C
F
0 5 10 15 20 25
Lag
-1.0
-0.5
0.0
0.5
1.0
30. P
A
C
F
0 5 10 15 20 25
Lag
-1.0
-0.5
0.0
0.5
1.0
A
C
F
Residual Normality Diagnostics for CO2(1)
QQ-Plot
-2 0 2
Quantile
-0.75
-0.50
32. rc
e
n
t
-0.96 -0.72 -0.48 -0.24 0 0.24 0.48
Residual
0
10
20
30
P
e
rc
e
n
t
Kernel
Normal
Monday, 21 June 2021 00:08:42 16
Distribution of Residuals for CO2(1)
33. -0.96 -0.8 -0.64 -0.48 -0.32 -0.16 0 0.16 0.32 0.48
Residual
0
10
20
30
P
e
rc
e
n
t
-0.96 -0.8 -0.64 -0.48 -0.32 -0.16 0 0.16 0.32 0.48
Residual
0
10
20
30
P
e
35. l
Monday, 21 June 2021 00:08:42 17
Model for variable CO2
Estimated Mean 0.000157
Period(s) of Differencing 1
Autoregressive Factors
Factor 1: 1 + 1.31265 B**(1) + 0.32245 B**(2)
Moving Average Factors
Factor 1: 1 + 0.98214 B**(1)
Warning: The ID value for observation 2 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 3 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 4 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 5 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 6 is the same as the ID
value for the last observation according to ID variable DATE.
36. Warning: The ID value for observation 7 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 8 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 9 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 10 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 11 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 12 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: There are gaps in the interval for observation 13
according to ID variable DATE.
Warning: The ID value for observation 14 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 15 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 16 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 17 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 18 is the same as the ID
value for the last observation according to ID variable DATE.
37. Warning: The ID value for observation 19 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 20 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 21 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 22 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 23 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 24 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: There are gaps in the interval for observation 25
according to ID variable DATE.
Warning: The ID value for observation 26 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 27 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 28 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 29 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 30 is the same as the ID
value for the last observation according to ID variable DATE.
38. Warning: The ID value for observation 31 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 32 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 33 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 34 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 35 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 36 is the same as the ID
value for the last observation according to ID variable DATE.
Monday, 21 June 2021 00:08:42 18
Warning: There are gaps in the interval for observation 37
according to ID variable DATE.
Warning: The ID value for observation 38 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 39 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 40 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 41 is the same as the ID
value for the last observation according to ID variable DATE.
39. Warning: The ID value for observation 42 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 43 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 44 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 45 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 46 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 47 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 48 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: There are gaps in the interval for observation 49
according to ID variable DATE.
Warning: The ID value for observation 50 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 51 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 52 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 53 is the same as the ID
value for the last observation according to ID variable DATE.
40. Warning: The ID value for observation 54 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 55 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 56 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 57 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 58 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 59 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 60 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: There are gaps in the interval for observation 61
according to ID variable DATE.
Warning: The ID value for observation 62 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 63 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 64 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 65 is the same as the ID
value for the last observation according to ID variable DATE.
41. Warning: The ID value for observation 66 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 67 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 68 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 69 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 70 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 71 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 72 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: There are gaps in the interval for observation 73
according to ID variable DATE.
Note: Further warnings will not be printed.
Warning: The ID value for observation 74 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 75 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 76 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 77 is the same as the ID
42. value for the last observation according to ID variable DATE.
Warning: The ID value for observation 78 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 79 is the same as the ID
value for the last observation according to ID variable DATE.
Monday, 21 June 2021 00:08:42 19
Warning: The ID value for observation 80 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 81 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 82 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 83 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 84 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 86 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 87 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 88 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 89 is the same as the ID
43. value for the last observation according to ID variable DATE.
Warning: The ID value for observation 90 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 91 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 92 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 93 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 94 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 95 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 96 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 98 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 99 is the same as the ID
value for the last observation according to ID variable DATE.
Warning: The ID value for observation 100 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 101 is the same as the
ID value for the last observation according to ID variable
DATE.
44. Warning: The ID value for observation 102 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 103 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 104 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 105 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 106 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 107 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 108 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 110 is the same as the
ID value for the last observation according to ID vari able
DATE.
Warning: The ID value for observation 111 is the same as the
ID value for the last observation according to ID variable
DATE.
45. Warning: The ID value for observation 112 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 113 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 114 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 115 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 116 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 117 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 118 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 119 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 120 is the same as the
ID value for the last observation according to ID variable
DATE.
46. Warning: The ID value for observation 122 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 123 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 124 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 125 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 126 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 127 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 20
Warning: The ID value for observation 128 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 129 is the same as the
ID value for the last observation according to ID variable
DATE.
47. Warning: The ID value for observation 130 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 131 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 132 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 134 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 135 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 136 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 137 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 138 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 139 is the same as the
ID value for the last observation according to ID variable
DATE.
48. Warning: The ID value for observation 140 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 141 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 142 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 143 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 144 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 146 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 147 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 148 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 149 is the same as the
ID value for the last observation according to ID variable
DATE.
49. Warning: The ID value for observation 150 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 151 is the same as the
ID value for the last observation according to ID variabl e
DATE.
Warning: The ID value for observation 152 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 153 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 154 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 155 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 156 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 158 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 159 is the same as the
ID value for the last observation according to ID variable
DATE.
50. Warning: The ID value for observation 160 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 161 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 162 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 163 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 164 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 165 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 166 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 167 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 168 is the same as the
ID value for the last observation according to ID variable
DATE.
51. Warning: The ID value for observation 170 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 171 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 172 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 173 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 174 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 175 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 21
Warning: The ID value for observation 176 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 177 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 178 is the same as the
52. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 179 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 180 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 182 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 183 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 184 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 185 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 186 is the same as the
ID value for the last observation according to ID vari able
DATE.
Warning: The ID value for observation 187 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 188 is the same as the
53. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 189 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 190 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 191 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 192 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 194 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 195 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 196 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 197 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 198 is the same as the
54. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 199 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 200 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 201 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 202 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 203 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 204 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 206 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 207 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 208 is the same as the
55. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 209 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 210 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 211 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 212 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 213 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 214 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 215 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 216 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 218 is the same as the
56. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 219 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 220 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 221 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 222 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 223 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 22
Warning: The ID value for observation 224 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 225 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 226 is the same as the
ID value for the last observation according to ID variable
57. DATE.
Warning: The ID value for observation 227 is the same as the
ID value for the last observation according to ID variabl e
DATE.
Warning: The ID value for observation 228 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 230 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 231 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 232 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 233 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 234 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 235 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 236 is the same as the
ID value for the last observation according to ID variable
58. DATE.
Warning: The ID value for observation 237 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 238 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 239 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 240 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 242 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 243 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 244 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 245 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 246 is the same as the
ID value for the last observation according to ID variable
59. DATE.
Warning: The ID value for observation 247 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 248 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 249 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 250 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 251 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 252 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 254 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 255 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 256 is the same as the
ID value for the last observation according to ID variable
60. DATE.
Warning: The ID value for observation 257 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 258 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 259 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 260 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 261 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 262 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 263 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 264 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 266 is the same as the
ID value for the last observation according to ID variable
61. DATE.
Warning: The ID value for observation 267 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 268 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 269 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 270 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 271 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 23
Warning: The ID value for observation 272 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 273 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 274 is the same as the
ID value for the last observation according to ID variable
DATE.
62. Warning: The ID value for observation 275 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 276 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 278 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 279 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 280 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 281 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 282 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 283 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 284 is the same as the
ID value for the last observation according to ID variable
DATE.
63. Warning: The ID value for observation 285 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 286 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 287 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 288 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 290 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 291 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 292 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 293 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 294 is the same as the
ID value for the last observation according to ID variable
DATE.
64. Warning: The ID value for observation 295 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 296 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 297 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 298 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 299 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 300 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 302 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 303 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 304 is the same as the
ID value for the last observation according to ID variable
DATE.
65. Warning: The ID value for observation 305 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 306 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 307 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 308 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 309 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 310 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 311 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 312 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 314 is the same as the
ID value for the last observation according to ID variable
DATE.
66. Warning: The ID value for observation 315 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 316 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 317 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 318 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 319 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 24
Warning: The ID value for observation 320 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 321 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 322 is the same as the
ID value for the last observation according to ID variable
DATE.
67. Warning: The ID value for observation 323 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 324 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 326 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 327 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 328 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 329 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 330 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 331 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 332 is the same as the
ID value for the last observation according to ID variable
DATE.
68. Warning: The ID value for observation 333 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 334 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 335 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 336 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 338 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 339 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 340 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 341 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 342 is the same as the
ID value for the last observation according to ID variable
DATE.
69. Warning: The ID value for observation 343 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 344 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 345 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 346 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 347 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 348 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 350 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 351 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 352 is the same as the
ID value for the last observation according to ID variable
DATE.
70. Warning: The ID value for observation 353 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 354 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 355 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 356 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 357 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 358 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 359 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 360 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 362 is the same as the
ID value for the last observation according to ID variable
DATE.
71. Warning: The ID value for observation 363 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 364 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 365 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 366 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 367 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 25
Warning: The ID value for observation 368 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 369 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 370 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 371 is the same as the
72. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 372 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 374 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 375 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 376 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 377 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 378 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 379 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 380 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 381 is the same as the
73. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 382 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 383 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 384 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 386 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 387 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 388 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 389 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 390 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 391 is the same as the
74. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 392 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 393 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 394 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 395 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 396 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 398 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 399 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 400 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 401 is the same as the
75. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 402 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 403 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 404 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 405 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 406 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 407 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 408 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 410 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 411 is the same as the
76. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 412 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 413 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 414 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 415 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 26
Warning: The ID value for observation 416 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 417 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 418 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 419 is the same as the
ID value for the last observation according to ID variable
77. DATE.
Warning: The ID value for observation 420 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 422 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 423 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 424 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 425 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 426 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 427 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 428 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 429 is the same as the
ID value for the last observation according to ID variable
78. DATE.
Warning: The ID value for observation 430 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 431 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 432 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 434 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 435 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 436 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 437 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 438 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 439 is the same as the
ID value for the last observation according to ID variable
79. DATE.
Warning: The ID value for observation 440 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 441 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 442 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 443 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 444 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 446 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 447 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 448 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 449 is the same as the
ID value for the last observation according to ID variable
80. DATE.
Warning: The ID value for observation 450 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 451 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 452 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 453 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 454 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 455 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 456 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 458 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 459 is the same as the
ID value for the last observation according to ID variable
81. DATE.
Warning: The ID value for observation 460 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 461 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 462 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 463 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 27
Warning: The ID value for observation 464 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 465 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 466 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 467 is the same as the
ID value for the last observation according to ID variable
DATE.
82. Warning: The ID value for observation 468 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 470 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 471 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 472 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 473 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 474 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 475 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 476 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 477 is the same as the
ID value for the last observation according to ID variable
DATE.
83. Warning: The ID value for observation 478 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 479 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 480 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 482 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 483 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 484 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 485 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 486 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 487 is the same as the
ID value for the last observation according to ID variable
DATE.
84. Warning: The ID value for observation 488 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 489 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 490 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 491 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 492 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 494 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 495 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 496 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 497 is the same as the
ID value for the last observation according to ID variable
DATE.
85. Warning: The ID value for observation 498 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 499 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 500 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 501 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 502 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 503 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 504 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 506 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 507 is the same as the
ID value for the last observation according to ID variable
DATE.
86. Warning: The ID value for observation 508 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 509 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 510 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 511 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 28
Warning: The ID value for observation 512 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 513 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 514 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 515 is the same as the
ID value for the last observation according to ID variable
DATE.
87. Warning: The ID value for observation 516 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 518 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 519 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 520 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 521 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 522 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 523 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 524 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 525 is the same as the
ID value for the last observation according to ID variable
DATE.
88. Warning: The ID value for observation 526 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 527 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 528 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 530 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 531 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 532 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 533 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 534 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 535 is the same as the
ID value for the last observation according to ID variable
DATE.
89. Warning: The ID value for observation 536 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 537 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 538 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 539 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 540 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 542 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 543 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 544 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 545 is the same as the
ID value for the last observation according to ID variable
DATE.
90. Warning: The ID value for observation 546 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 547 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 548 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 549 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 550 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 551 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 552 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 554 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 555 is the same as the
ID value for the last observation according to ID variable
DATE.
91. Warning: The ID value for observation 556 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 557 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 558 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 559 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 29
Warning: The ID value for observation 560 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 561 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 562 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 563 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 564 is the same as the
92. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 566 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 567 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 568 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 569 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 570 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 571 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 572 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 573 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 574 is the same as the
93. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 575 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 576 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 578 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 579 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 580 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 581 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 582 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 583 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 584 is the same as the
94. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 585 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 586 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 587 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 588 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 590 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 591 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 592 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 593 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 594 is the same as the
95. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 595 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 596 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 597 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 598 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 599 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 600 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 602 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 603 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 604 is the same as the
96. ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 605 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 606 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 607 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 30
Warning: The ID value for observation 608 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 609 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 610 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 611 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 612 is the same as the
ID value for the last observation according to ID variable
97. DATE.
Warning: The ID value for observation 614 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 615 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 616 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 617 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 618 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 619 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 620 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 621 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 622 is the same as the
ID value for the last observation according to ID variable
98. DATE.
Warning: The ID value for observation 623 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 624 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 626 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 627 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 628 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 629 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 630 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 631 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 632 is the same as the
ID value for the last observation according to ID variable
99. DATE.
Warning: The ID value for observation 633 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 634 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 635 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 636 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 638 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 639 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 640 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 641 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 642 is the same as the
ID value for the last observation according to ID variable
100. DATE.
Warning: The ID value for observation 643 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 644 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 645 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 646 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 647 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 648 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 650 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 651 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 652 is the same as the
ID value for the last observation according to ID variable
101. DATE.
Warning: The ID value for observation 653 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 654 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 655 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 31
Warning: The ID value for observation 656 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 657 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 658 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 659 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 660 is the same as the
ID value for the last observation according to ID variable
DATE.
102. Warning: The ID value for observation 662 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 663 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 664 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 665 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 666 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 667 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 668 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 669 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 670 is the same as the
ID value for the last observation according to ID variable
DATE.
103. Warning: The ID value for observation 671 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 672 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 674 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 675 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 676 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 677 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 678 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 679 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 680 is the same as the
ID value for the last observation according to ID variable
DATE.
104. Warning: The ID value for observation 681 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 682 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 683 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 684 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 686 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 687 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 688 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 689 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 690 is the same as the
ID value for the last observation according to ID variable
DATE.
105. Warning: The ID value for observation 691 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 692 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 693 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 694 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 695 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 696 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 698 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 699 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 700 is the same as the
ID value for the last observation according to ID variable
DATE.
106. Warning: The ID value for observation 701 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 702 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 703 is the same as the
ID value for the last observation according to ID variable
DATE.
Monday, 21 June 2021 00:08:42 32
Warning: The ID value for observation 704 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 705 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 706 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 707 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 708 is the same as the
ID value for the last observation according to ID variable
DATE.
107. Warning: The ID value for observation 710 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 711 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 712 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 713 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 714 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 715 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 716 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 717 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 718 is the same as the
ID value for the last observation according to ID variable
DATE.
108. Warning: The ID value for observation 719 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 720 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 722 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 723 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 724 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 725 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 726 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 727 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 728 is the same as the
ID value for the last observation according to ID variable
DATE.
109. Warning: The ID value for observation 729 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 730 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 731 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 732 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 734 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 735 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 736 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 737 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 738 is the same as the
ID value for the last observation according to ID variable
DATE.
110. Warning: The ID value for observation 739 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 740 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 741 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 742 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 743 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 744 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 746 is the same as the
ID value for the last observation according to ID vari able
DATE.
Warning: The ID value for observation 747 is the same as the
ID value for the last observation according to ID variable
DATE.
Warning: The ID value for observation 748 is the same as the
ID value for the last observation according to ID variable
DATE.