Model Reuse With Bike Rental Station

Model Reuse With Bike Rental Station
Model Reuse with Bike Rental Station Data Authors: 1.Arun Bala Subramaniyan, M.S. Industrial Engineering, Arizona State University. 2. Dr. Rong
Pan, Associate Professor of Industrial Engineering, Arizona State University. Introduction and Motivation Bike Rental Stations are a good business
in places with large number of tourists and also the native people rent bikes for their day to day work. In this project, the bike rental station located
in Valencia, the third largest city of Spain is considered. The bike rental company would like to predict the number of bikes available in each station
three hours in advance. There are atleast two uses for such prediction. At first, a user plans to rent (or return) a bike in 3 hours time and wants to... Show
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This process is continued for selecting the best model for all the new stations (201 to 275). The R software package is used for this purpose. The
extracted best models for the new stations are stored in .csv file. In some cases, the prediction result is negative or it exceeds the maximum limit of
the bikes that can be parked in a station. This can be overcome by adding a constraint such that whenever the result is negative, the value is reset to
zero and whenever the result exceeds the maximum limit, the value is reset to the number of docks at the particular station. So, this helps in
reducing the error value. Prediction Using the extracted models, the number of bikes at the new stations is predicted. The same constraints are
applied to avoid negative values and over fitting. The R software is used for predicting the number of bikes. The results of this prediction are stored
in .csv file. Other Methods tried for prediction Instead of reusing the trained models, new models were built with the given deployment data for
stations 201 to 275. Some of the methods used are given below. Ordinary Least Squares Method After collecting and cleaning the data, the first model
was built using all the regressors under consideration. A thorough analysis of this full model, including residual analysis and multicollinearity check
was done. The best subset regression was also tried. The normal probability
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Essay on SCMS 7110 Exam 2 Solutions
Question 1 4 / 4 points The overall upward or downward pattern of the data in an annual time series will be contained in the ____________
component. trend cyclical irregular seasonal Question 2 4 / 4 points When using the exponentially weighted moving average for purposes of forecasting
rather than smoothing, the previous smoothed value becomes the forecast. the current smoothed value becomes the forecast. the next smoothed value
becomes the forecast. None of the above. Question 3 4 / 4 points The following is the list of MAD statistics for each of the models you have estimated
from time–series data: Model MAD Linear Trend 1.38 Quadratic Trend 1.22 Exponential Trend 1.39 AR(2) 0.71 Based on the MAD criterion,
... Show
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The business literature involving human capital shows that education influences an individual's annual income. Combined, these may influence family
size. With this in mind, what should the real estate builder be particularly concerned with when analyzing the multiple regression model?
Randomness of error terms Collinearity Missing observations Normality of residuals Question 8 4 / 4 points In multiple regression, the __________
procedure permits variables to enter and leave the model at different stages of its development. forward selection residual analysis backward
elimination stepwise regression Question 9 4 / 4 points A regression diagnostic tool used to study the possible effects of collinearity is the slope. the
Y–intercept. the VIF. the standard error of the estimate. Question 10 4 / 4 points The Variance Inflationary Factor (VIF) measures the correlation of
the X variables with the Y variable. correlation of the X variables with each other. contribution of each X variable with the Y variable after all other
X variables are included in the model. standard deviation of the slope. Question 11 4 / 4 points TABLE 15–3 In Hawaii, condemnation proceedings are
under way to enable private citizens to own the property that their homes are built on. Until recently, only estates were permitted to own land, and
homeowners leased the land from the estate. In order to comply with the new law, a large Hawaiian

Statistical Concepts Of A Real World Business Situation
1. Introduction "Statistics is a mathematical science concerned with the collection, presentation, analysis and interpretation or explanation of data."
(Black et. al, 2013). This report aims to apply statistical concepts to a real world business situation. A multiple regression model is applied to the data
in order to try and predict the changes in stock price of the selected company, and the goodness of fit of the data to the model is critically analysed by
testing the overall model, conducting significance tests of the regression coefficients, coefficient of multiple determination (RВІ and Adjusted RВІ).
Finally, a prediction is made with a confidence interval estimate in order to analyse if the applied model and data are useful for the... Show more content
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al, 2010). In the ANOVA table above, the F statistic has a value of 8.9250 with a p
–value of 3.01602E–19 (very close to 0). Since the p–value < О± =
0.05, we can reject the null hypothesis and conclude that at least one of the independent variables in the dataset is significant in predicting the change
in stock prices of HollyFrontier Corporation. 5. Testing the Regression Coefficients CoefficientsStandard Errort StatP–value
Intercept0.446703140.0489992349.1165330632.24627E–18 Year_x_Natural_gas0.4384481240.1810737482.4213787440.015838578
30year_x_Gold–0.3556329880.092061454–3.8629955650.000127684
30year_acc1_x_Copper_vel40.3752800920.0920562584.0766385535.36313E–05
30year_acc1_x_West_Texas_vel4–0.2446587730.08932177–2.7390721540.00639495
Aluminium_vel2_x_Copper_vel1–0.14771280.059326221–2.4898400220.013123889
Baltic_vel4_x_West_Texas_acc20.2371417630.0682766553.4732481290.000561658
Copper_vel3_x_HFC_acc2–0.3969403250.081145963–4.8916829591.37459E–06
Copper_vel3_x_SPDR_XOP0.1748641530.083294852.0993393180.036318588
Copper_vel3_x_West_Texas_vel20.3204390240.0843822833.7974680640.000165301

Portfolio Analysis On Vanguard, Reynolds And Hasbro
Xiaoling Tang
1654 Aspen Ct. Apt 237
Kent, OH
44240
July 27, 2015
Ms. Sharpe
3737 Cascades Blvd #206
Kent, OH
44240
Dear Ms. Sharpe:
RE: PORTFOLIO ANALYSIS ON VANGUARD, REYNOLDS AND HASBRO
Investment decision is an intricate process that requires careful analysis of individual investment options available for continued profitability. Different
financial analyses provide different perspectives. While ratio analysis of an individual company offers the financial health of that company, its
comparison with other key investment opportunities may be limited when comparing across different industries. This analysis seeks to utilize a
number of statistical and financial results emerging from the analysis of 5 years of data. The results will be analyzed and discussed per individual
subheading used in the analysis to provide a broader picture.
The mean of Vanguard is 0.57433 as compared to RJR and Hasbro whose means are significantly high at 1.87483333 and 1.18383. However, the
standard deviations of the three variables indicate that Vanguard has the loweststandard deviation at 3.60171 as compared to 9.36645828 and 8.11583
for RJR and Hasbro respectively. The lowest standard deviation as in the case of Vanguard indicates that the data is more reliable and provides a more
realistic data set than the other data sets. Thus, Vanguard provides the best investment opportunity. The median of the data may not provide a very
accurate description of the variables although Vanguard's

Ap Statistics Outline
Stats: Modeling the World– Bock, Velleman, & DeVeaux Chapter 1: Stats Starts Here Chapter 2: Data Key Vocabulary: Statistics data, datum
variation individual respondent subject participant experimental unit observation variable categorical quantitative Calculator Skills: enter data in a
list change a datum delete a datum name a new list clear a list delete a list recreate a list copy a list 1. Name three things you learned about Statistics
in Chapter 1. 2. The authors claim that this book is very different from a typical mathematics textbook. Would you agree or disagree, based on what
you read in Chapter 1? Explain. 3. According to the authors, what are the "three simple steps to doing Statistics right?" 4. What... Show more content on
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5. When is it more appropriate to use the median as a measure of center rather than the mean? Why? 6. When do the mean and median have the same
value? 7. Describe the relationship between variance and standard deviation. Chapter 5: Describing Distributions Numerically Stats: Modeling the
World – Bock, Velleman, & DeVeaux Chapter 6: The Standard Deviation as a Ruler and the Normal Model Key Vocabulary: standard deviation
standardized value rescaling z–score normal model parameter statistic standard Normal model 68–95–99.7 Rule normal probability plot N( , )
Calculator Skills: normalpdf( normalcdf( invNorm( normal probability plot –1E99 and 1E99 1. What unit of measurement is used to describe how far a
set of values are from the mean? 2. Explain how to standardize a value. 3. Briefly describe why standardized units are used to compare values that
are measured using different scales, different units, or different populations. 4. How does adding or subtracting a constant amount to each value in a
set of data affect the mean? Why does this happen? 5. How does multiplying or dividing a constant amount by each value in a set of data (also called
rescaling) affect the mean? Why does this happen? 6. How does adding or subtracting a constant amount to each value in a set of data affect the
standard deviation? Why does this happen? 7. How does multiplying or dividing a constant amount by each value in a set of data (also called rescaling)

Eco550 Assignment 1
Assignment 1: Making Decisions Based on Demand and Forecasting
Managerial Economics and Globalization, ECO550
Making Decisions Based on Demand and Forecasting
A market demand analysis is used to help understand how much consumer demand there is for a given product or service. This type of analysis will
help determine if a business can successfully enter a market and generate enough revenue and profit to maintain the business. One must identify the
market and the growth potential. Domino's Pizza was incorporated in 1963 and has been franchising since 1967. A traditional Domino's store is
located in shopping centers and/or strip malls with appropriate parking for delivery vehicles and walk–in customers for ... Show more content on
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The position coefficient shows that as the independent variable changes the quantity demanded changes in the same direction. Using the data
collected on Morehead City in another example: | Domino's Pizza = Growth Forecast based on Pizza Price| | | | | | | | | | | | | | | | Regression Statistics| | | | | |
| | Multiple R| 0.996320672| | | | | | | | R Square| 0.992654881| | | | | | | | Adjusted R Square| 0.985309761| | | | | | | | Standard Error| 328.6398738| | | | | | | |
Observations| 3| | | | | | | | | | | | | | | | | ANOVA| | | | | | | | | | df| SS| MS| F| Significance F| | | | Regression| 1| 14596204.5| 14596204.5| 135.144828|
0.054627666| | | | Residual| 1| 108004.1667| 108004.167| | | | | | Total| 2| 14704208.67| | | | | | | | | | | | | | | | | Coefficients| Standard Error| t Stat| P–value|
Lower 95%| Upper 95%| Lower 95.0%| Upper 95.0%| Intercept| 28020.81833| 1635.404715| 17.133874| 0.03711352| 7241.031202| 48800.6055|
7241.0312| 48800.6055| X Variable 1|–2701.5| 232.3834833| –11.6251808| 0.05462767| –5654.212117| 251.212117| –5654.21212| 251.212117| | | | | | |
| | | | | | | | | | | | | | | | | | | | | | RESIDUAL OUTPUT| | | | | | |

The Assessment Of Osborne And Waters
In statistical tests, we must rely on assumptions regarding the variables we used in the analysis. If these assumptions are not met we may arrive at
results that are incorrect, or not representative of the population, typically due to a Type I or a Type II error, or an over or under estimation of
significance or effect size. Osborne and Waters (n.d., p. 1) quote an 1997 article by Pedhazur stating "Knowledge and understanding of the situations
when violations of assumptions lead to serious biases, and when they are of little consequence, are essential to meaningful data analysis" which while a
very important point, really only holds importance when researchers test assumptions, an important step in data analysis that is rarely performed....
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These tests give researchers information about normality, while the K–S tests provide inferential statistics on normality. One of the best tests for
outliers is a visual inspection of histograms, as well as frequency distributions or converting data to z–scores. The removal of univariate and bivariate
outliers can reduce the probability of Type I and Type II errors, which improve the accuracy of some estimates. It is important to consider that removing
outliers is not always desirable, in which case transformations can improve normality. This can complicate the interpretation of the results, and therefore
should only be used deliberately and in an informed manner. To accurately estimate the relationship between dependent and independent variables
using standard multiple regression, these relationships must be linear in nature. This is why it is so important to examine analyses for non–linear data,
as non–linear data will result in a regression analysis that under–estimates the true relationship. Under–estimation carries some risk, in particular an
increased chance of Type II errors for the independent variable, and in the case of multiple regression, an increased risk of Type I errors
(over–estimation) for other independent variables that share variance with that variable. There are a few primary ways to detect non–linearity in the
data. The use of theory or previous research can be used to inform current analyses,

Using Stata for Principles of Econometrics
Using Stata For Principles of Econometrics . Third Edition I В·1В· I ! t . i: f, I Lee Adkins dedicates this work to his lovely and loving wife, Kathy ,
Carter Hill dedicates this work to Stan Johnson and George Judge – ' , . Bicentennial Logo Design: Richard 1. Pacifico Copyright @ 2008 John Wiley
& Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means,
electronic, mechanical, photocopying, recording, scanning, or otherwise, exC;ept as permitted under Sections 107 or 108 of the 1976 United States
Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of... Show more content on
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2.4:1 Fitted values and residuals 63 2.4.2 Computing an elasticity 65 2.4.3 Plotting the fitted regression line 67 2.4.4 Estimating the variance of the
error term 70 2.4.5 Viewing estimated

Another reason for choosing the FE model12 is that it can...
Another reason for choosing the FE model12 is that it can solve the endogeneity problem through using the FE–IV model; the variable GDP per
capita–used as a proxy of income–could be an endogenous variable. An endogenous variables are variables that correlated with the error term
(ОµаЇњаЇ§ ), while the variables that uncorrelated with the error term are called exogenous variables. The description of these terms explains that an
endogenous variable is determined within the model itself while an exogenous variable is determined outside the model. To understand the
endogeneity, we will use the classic regression equations that show the relationship between prices and wages: Price= Яљ0 + Яљ1Wage + ОµаЇњаЇ§
.............................. (11) Wage = Яљ0аЇ” + Яљ1аЇ•Price+... Show more content on Helpwriting.net ...
As we reject the HВ°, the random effect model will produce biased estimates, so the FE model is used alternatively. In FE, the П„аЇњ is correlated
with the regressor ЬєаЇњаЇ§ . 13 RGDP per capita is endogenous variable in our model because of reverse causality; the effect between corruption and
RGDP goes in both directions; either RGDP affects corruption or corruption affects RGDP. 19 Chapter 3 Empirical Evidence estimator is biased even
in large sample and the FE–IV t–statistic and confidence intervals are not true. In this paper, we will use one instrument which is the RGDP of the
great importer in 2000. Here is a description why I choose this instrument. First, a country that buys most of other countryвЂџs export can definitely
increase the GDP of that country, as the exports will be included in the countryвЂџs GDP calculations. Second, there is no any reason for the GDP of
one country to affect the corruption level of another country, which means; there is no any correlation between the GDP of great importer and the
domestic factors in the country at which it buys most of its exports. 3.3.3 Regression Results and Discussion Although some of the results in this paper
support that in previous papers, but it shows new other findings. Using FE– IV estimation technique through using the RGDP of great importer as an
instrument for RGDP per capita, we find that, income, political stability and

Econometrics. a Regression Analysis
Question 1: Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error
of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of
dollars and LOT is measured in acres. Model 1: OLS estimates using the 832 observations 1–832 Dependent variable: price VARIABLE
COEFFICIENT STDERROR T STAT P
–VALUE const 119.575 1.54566 77.362 <0.00001 *** lot 1.38850 0.209083 6.641 <0.00001 *** Mean of
dependent variable = 122.076 Standard deviation of dep. var. = 44.3478... Show more content on Helpwriting.net ...
If this is present it means there is a violation of the constant variance assumption. * The effect of heteroskedasticity on the OLS estimator is that it is
still unbiased. * The effect of heteroskedasticity on the OLS estimator standard errors are that the results in adjusted robust standard errors cause the
homoskedasticity results to be incorrect standard errors. Question 5: As mentioned in class, one commonly employed solution to heteroscedasticity is
to adjust the standard errors for the possible presence of heteroskedasticity, i.e. we compute the heteroskedasticity–robust standard errors, which are also
referred to as heteroskedasticity–consistent standard errors. Rerun the regression in part (2) with the OLS standard errors replaced by
heteroskedasticity–robust standard errors. Comment on the differences between the OLS standard errors in part (2) and the heteroskedasticity–robust
standard errors in this part. * With Homoskadasticity, Part 2 model, with constant variance of error term: Model 2: OLS estimates using the 832
observations 1–832 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P
–VALUE const 34.6160 4.74177 7.300 <0.00001
*** lot 1.71129 0.148643 11.513 <0.00001 *** bdrm 3.39579 1.36729 2.484 0.01320

Modeling Of Forecasting Inflation On Nepal Essay
CHAPTER FOUR
MODELING OF FORECASTING INFLATION IN NEPAL
4.1Introduction
Inflation is a burning economic problem in the developing countries like Nepal that brings adverse effects like loss of purchasing power of national
currency, leading to the aggravation of social conditions and living standards. This also leads to uncertainty making domestic and foreign investors
reluctant to invest in the economy. Additionally,inflation broadens the country's terms of trade causing domestic goods and services more expensive in
the market. That is why; the monetary authority of every economy should have the objective of maintaining stable price.
Inflation forecasting plays a central role in monetary policy formulation. Recent international empirical evidence suggests that with the decline in
inflation of recent years, a fairly widespread phenomenon, the combined dynamics of this variable and its potential predictors, such as money or
different measures of the output gap, has changed, and inflation has become more unpredictable. Univariate models tend to show a better forecasting
capacity than those based on various inflation theories, such as the Phillips curve. Traditionally, in industrialized countries the Phillips curve has played
a predominant role in inflation forecasting, and according to Stock and Watson (1999), Atkenson and Ohanian (2001) and Canova, (2002), it would
seem to perform better in terms of forecasting error than other alternative models. In recent years there have

Sata Commands
Some Stata Commands Last modified: January 2, 2006 9:51AM General Plotting Commands 1. Plot a histogram of a variable: histogram vname 2.
Plot a histogram of a variable using frequencies: histogram vname, freq histogram vname, bin(xx) norm where xx is the number of bins. 3. Plot a
boxplot of a variable: graph box vname 4. Plot side–by–side box plots for one variable (vone) by categories of another variable vtwo. (vtwo should be
categorical)): graph box vone, over(vtwo) 5. A scatter plot of two variables: scatter vone vtwo 6. A matrix of scatter plots for three variables: graph
matrix vone vtwo vthree 7. A scatter plot of two variables with the values of a third variable used in place of points on... Show more content on
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Identify points with largest and smallest residuals: sort residuals list in 1/5 list in –5/l (The last command is "minus 5" / "lowercase letter L".) 7.
Compute multiple regression equation (vy is response, vthree, vtwo, and vvthree are explanatory variables): regress vy vone vtwo vthree Important
Notes on the "stem" command In some versions of Stata, there is a potential glitch with Stata 's stem command for stem–and–leaf plots. The stem
function seems to permanently reorder the data so that they are sorted according to the variable that the stem–and–leaf plot was plotted for. The best
way to avoid this problem is to avoid doing any stem–and–leaf plots (do histograms instead). However, if you really want to do a stem–and–leaf plot
you should always create a variable containing the original observation numbers (called index, for example). A command to do so is: generate index =
_n If you do this, then you can re–sort the data after the stem–and–leaf plot according to the index variable: sort index. Then, the data are back in the
original order. Summary of These and Other Commands Here is a list of the commands demonstrated above and some other commands that you may
find useful (this is by no means an exhaustive list of all Stata commands): anova| general ANOVA, ANCOVA, or regression| by| repeat operation for
categories of a variable| ci| confidence intervals

The Springville Herald Case
The Springville Herald Case
SH2.1 Type of ErrorTotalPercent Copy Error5416.9 Layout72.2 Omits134.1 Paste–up113.4 Poor reproduction82.5 Ran–in error309.4 Rate quote134.1
Space not needed72.2 Typesetting5316.6 Velox288.8 Wrong ad257.8 Wrong date144.4 Wrong position4514.1 Wrong manual paste
–up51.6 Wrong
size61.9 Total319100.0
SH2.2 (a, b)If we are focusing on quality improvement, we would want to determine the categories which a responsible for the highest proportion of
errors. Thus, a Pareto diagram would be most appropriate. ... Show more content on Helpwriting.net ...
Therefore, we might wish to use the pie chart in this case. (c) Because we want to focus on what proportion of the whole is in each category.
SH2.8(d) Almost ninety percent of the dollar amount of ran–in errors are attributable to policy. cont. (e) The reasons for the policy explanation should
be determined and policies should be either changed or clear operational definitions should be developed.
SH2.9 (a)
| | | |Stem–and–Leaf Display |
| | | |for Calls | |
| | | |Stem unit: |10 |
| | | | | |
|Statistics | |1 |1 7 8 |
|Sample Size |90 | |2 |3 6 7 8 9 |
|Mean |51.84444 | |3 |0 1 3 3 4 5 5 7 8 8 8 8 9 |
|Median |49

Stat Project
| Determinants of Profit in Various Supermarkets.| | | Submitted by:| Date: | | Abstract: The research statistically determines the profits of supermarkets
based on the sales of food items, non–food items and size of supermarket. The regression model was done on both models to determine that in both
models, increase in sales and size of stores increases the overall profit. However the model with independent variables sales of food items, sales of
non–food items and size of stores is the more relevant model. Introduction: There are 10 supermarkets with different kinds of products. These products
are food products and non–food products. The paper wants to understand the relation between the profits these... Show more content on Helpwriting.net
...
It is positive and very close to 1. This means that this model is Adjusted R square is 96 % meaning that 96 % of change in the profit can be
explained by these 2 variables. The F Significance value is negligible meaning that the model is very significant. The regression line formula is: Profit
= 3.75 + 0.04*Non–food sales + 0.63* store size It means that with every 1 dollar increase in non–food items sales, the profit increases by 0.04 dollars.
And with each 1 unit increase in the size of the

Multiple Regression Analysis Exam For Pathology Severity,...
Module
PS71020D
MSc Statistics Coursework
2016–2017
Module co–ordinator
Dr Devin Terhune
Candidate number
33440401
Title
Multiple Regression Analysis Exam
Word count
1242
Results
Delusional ideation
A multiple regression analysis was run to predict delusional ideation from pathology severity, perception, memory, speak vs. hear, and imagine vs.
hear with forced entry. There was linearity as assessed by partial regression plots and a plot of studentized residuals against the predicted values. There
was independence of residuals, as assessed by a Durbin–Watson statistic of 2.011. There was homoscedasticity, as assessed by visual inspection of a
plot of studentized residuals versus unstandardized predicted values. There was evidence ... Show more content on Helpwriting.net ...
There were no studentized deleted residuals greater than В±3 standard deviations, no leverage values greater than 0.2, and values for Cook 's distance
above 1. The assumption of normality was met, as assessed by Q–Q Plot. The multiple regression model statistically significantly predicted
hallucination history, F(4, 175) = 89.89, p < .005, adj. R2 = 66.5%. All variables added statistically significantly to the prediction, p < .05. Regression
coefficients and standard errors can be found in Table 2 (below).

Table 2: Summary of Multiple Regression Analysis (Hallucination history)
Multiple regression analysis was run to predict hallucination history from metacognition variables; perception and memory. The model statistically
significantly predicted hallucination history, F(2, 177) = 11.88, p < .000, adj. R2 = 10.8%. All variables added statistically significantly to the
prediction, p < .05
Multiple regression analysis was run to predict hallucination history from source monitoring variables; speak vs. hear and imagine vs. hear. The
model statistically significantly predicted hallucination history, F(2, 177) = 171.7, p < .000, adj. R2 = 65.6%. All variables added statistically
significantly to the prediction, p < .05
Table 3: Correlation Matrix for Hallucination history
Discussion
The prediction model for delusional ideation was not statistically

A Report On Engle Granger Cointegration Test
4. Empirical Results In this section, we discuss our findings of Engle–Granger cointegration test which we applied in order to identify whether there is
cointegration relationship between dependent variable – the real non–oil GDP and independent variables – real credit to the private sector and non–oil
sector real effective exchange rate. The steps of the EG approach have been undertaken in order to obtain the long–run model that explains the
relationship between these variables. 4.1. Unit Root Test First of all, variables should be given in log levels in order to alleviate the problem of serial
correlation and the elasticity of the coefficients. The results of ADF unit root test in levels concludes that all three variables– seasonally... Show more
content on Helpwriting.net ...
Table Variable nameADF test (1% critical value =–3.557472, N=56), H0: [has a unit root]Inference t–StatisticProb.*ln_rgdp_noil_sa
–0.2028770.9314I(1) ln_rcred_to_ps –0.8740360.7892I(1) ln_reer_noil –0.5072430.8815I(1) 5% critical value =–3.557472, N=55,
t=0d(ln_rgdp_noil_sa) –11.601100I(0) d(ln_cred_to_ps) –9.0907840I(0) dln_reer_noil) –5.6490220I(0) Sample: 2000Q1:2013Q4 In the Table , d
stands for 1st difference, such that d(ln_rgdp_noil_sa) is the result of the 1st difference ADF unit root test on seasonally adjusted real non–oil GDP and
etc. The graphs below show the trend of the three series through the period from 2000 to 2013 based on level and 1st difference Augmented Dickey
Fuller unit root tests, respectively. Figure Figure ADL and Optimal Lag Selection: From General to Specific After checking for stationarity,
autoregressive distributed lag (ADL) models are estimated and the proper lag length is chosen so as to make the residuals of our model white noise.
As can be seen in the tables on ADLs in Appendix 1, all the model specifications' residuals according to the Jarque–Bera Histogram–Normality tests,
Breusch–Godfrey serial correlation LM tests, and Breusch–Pagan–Godfrey Heteroskedasticity tests are normally distributed, serially uncorrelated and
homoscedastic, respectively. It shows that all residual diagnostic parameters are satisfactory for estimating our model. Therefore, the

Sensor Faults Essay
I. INTRODUCTION WITH the rapid development of sensor technique and its growing lower cost, a great number of sensors are installed in modern
industrial processes for measuring, monitoring and controlling purpose. This inevitably increases the probability of sensor faults. Therefore, early
detection of sensor faults is essential to avoid performance degradation and damage to equipment. Over the past decades, research on process
monitoring and fault detection (PM–FD) has attracted considerable attention. Model–based and data–driven methods are two widely–used types of FD
techniques [1]–[4]. With the available data measurements, data–driven methods attracts increasing attention. Because the sensor measurements are
highly correlated due to the... Show more content on Helpwriting.net ...
While the precision degradation fault disturbs the variance/covariance of process measurements. Compared with the large amount of research work
implicitly or explicitly focused on the detection of sensor fault type with mean vector change [17], [18], the studies on sensor precision degradation are
relatively few [14], [19]. Qin et.al. [20] proposed a subspace identification model for detecting and identifying faulty sensors, including precision
degradation type of fault. Wan et.al. [21] studied the diagnosis of sensor precision degradation in the presence of control by minimizing the disturbance
variance. Furthermore, the successful application of CCA–based method constraints to the assumption that the residual signal follows a Gaussian
distribution. In practice, fault detection is much more challenging when the processes with complicated non–Gaussian [22]. To deal with non–Gaussian
challenges, some variations of the existing MVA–methods have been developed. Most of them first estimate a signal distribution and then set a
threshold based on the estimated distribution for FD purpose. We refer these methods as distribution estimationbased method, such as Gaussian
Mixture Models (GMM)– based approaches [23], [24], kernel–based ones [25] and sequential quantile estimation–based ones [26]. Although these
approaches have applied successfully in these complicated processes, their performance in FD are commonly limited by the selection of kernel
parameters and other specified

Financial Regressiom Essay
FINA 6204 Problem Set 1 The purpose of the assignment is to review basic hypothesis testing and regression techniques. There is an appendix in
your textbook, Appendix C: Using Excel to Conduct Analysis, which may help you with running regressions in Microsoft Excel. You may also wish
to use a basic statistics text for guidance if needed. I have also provided you with a table with the t distribution. If you have an older version of
EXCEL and have not previously loaded the Analysis ToolPak, go to TOOLS, ADD
–INS, Analysis Tool Pak. This will load the regression software that
you will need. Then go to TOOLS, DATA ANALYSIS, Regression. Now you are ready to run regressions in EXCEL. Alternatively, if you have the most
... Show more content on Helpwriting.net ...
Excess rate of return (firm) = Rate of return (firm) – Risk free rate. 1b.Determine the alpha and beta coefficients for this stock by running a simple
linear regression. Use the file from part (1a) and regress the excess rate of return for the firm against the excess rate of return for the market. The
"excess rate of return for the firm" data is the Input Y Range (dependent variable) and the "excess rate of return for the market" is the Input X Range
(In Excel, the Data Analysis menu is under Tools (older version of Excel) or Data (newer version)). If you include the row with the variable name in
your Input Y Range and your Input X Range, check the box LABELS, and Excel will automatically name your

Bivariate Regression
Linear Regression Models
1
SPSS for WindowsВ® Intermediate & Advanced Applied Statistics Zayed University Office of Research SPSS for WindowsВ® Workshop Series
Presented by Dr. Maher Khelifa Associate Professor Department of Humanities and Social Sciences College of Arts and Sciences
© Dr. Maher Khelifa
2
Bi–variate Linear Regression
(Simple Linear Regression)
Understanding Bivariate Linear Regression
3
п‚— Many statistical indices summarize information about particular
phenomena under study.
п‚— For example, the Pearson (r) summarizes the magnitude of a linear
relationship between pairs of variables.
п‚— However, one major scientific research objective is to "explain",

"predict", or ... Show more content on Helpwriting.net ...
The parameters ОІ0 and ОІ1 are constants describing the functional relationship in the population. The value of ОІ1 identifies the change along the Y
scale expected for every unit changed in fixed values of X (represents the slope or degree of steepness). The values of ОІ0 identifies an adjustment
constant due to scale differences in measuring X and Y (the intercept or the place on the Y axis through which the straight line passes. It is the value
of Y when X = 0). в€‘ (Epsilon) represents an error component for each individual. The portion of Y score that cannot be accounted for by its
systematic relationship with values of X.
пѓ·
пѓ·
пѓ·
пѓ·
Understanding Bivariate Linear Regression
12
The formula Y = ОІ0 + ОІ1X + Оµ can be thought of as:
пѓ·
Yi = Y'+ Оµi (where О± + ОІ1Xi define the predictable part of any Y score for fixed values of X. Y' is considered the predicted score).
The mathematical equation for the sample general linear model is represented as:
пѓ·
Yi = b0 + b1Xi + ei.

In this equation the values of a and b can be thought of as values that maximize the explanatory power or predictive accuracy of X in relation to Y. In
maximizing explanatory power or predictive accuracy these values minimize prediction error. If Y represents an individual's score on the criterion
variable and Y' is the predicted score, then Y–Y' = error score (e) or the

Analysis Of Proposition Of Rational Expectations,...
CHAPTER ELEVEN
STUDY WITH INVARIANCE PROPOSITION OF RATIONAL EXPECTATIONS
11.1 Introduction The concept of Invariance Proposition of Rational Expectations, developed by Lucas, Sargent and Wallace in early seventies, presents
the idea that the anticipated part of money supply affects price level. Since the present work is devoted to study the relationship between money supply
and price level, the Invariance Proposition theory of rational expectation can be applied to examine the relationship between anticipated money supply
and price level. In order to apply Invariance Proposition theory in examining the impact of anticipated money supply on price level, we need to estimate
the anticipated money supply.
There are several procedures to estimate the anticipated money supply; the present study has applied ARIMA structures of narrow and broad money
supply for the estimation of anticipated money supply. After identifying anticipated money supply, a regression equation has been performed taking
price level as dependent variable and anticipated money supply as explanatory variable.
11.2 ARIMA Model for M1 Money Supply
In order to quantify anticipated money supply, the ARIMA model has been applied. For this purpose equation (11.1) has been employed for ARIMA
structure for M1 money supply, on the basis of which the anticipated M1 money supplies has been quantified. гЂ–dLnMгЂ—_1t=О±+ОІ_1
гЂ–dLnMгЂ—_(1t–1)+ОІ_2 гЂ–dLnMгЂ—_(1t–2)+в‹Ї+ОІ_k гЂ–dLnMгЂ—_(1t–k)+Оё_1 u_(t–1)+Оё_2 u_(t–2)+в‹Ї+гЂ–

Grad Papaer
Property Crimes Case Study # 49
Applied Managerial Statistics: GM533
Virginia Davis, Lauren Holder, Stanley Philip and Andrea Watson
Executive Summary
The Property Crimes study examined data provided by various U.S. government agencies on crime rates in the fifty U.S. states. Other data studied were
eight possible contributing factors such as per capita income, high school dropout rate, average precipitation, population density, and urbanization.
Analysis revealed, of the eight possible contributing factors, three of those variables (urbanization rate, high school dropout rate and population
density) affected property crime rates. Of the given data, the model accounted for approximately 66% of the contributing factors ... Show more content
on Helpwriting.net ...
–––––––––––––
KIDS 1.104 1.449 0.76 0.450
–––––––––––––––––––––––––––––––––––––––––––––––––
PRECIP 1.58 11.16 0.14 0.888
–––––––––––––––––––––––––––––––––––––––––––––––––
UNEMPLOY –46.38 79.65 –0.58 0.564
–––––––––––––––––––––––––––––––––––––––––––––––––
URBAN 64.39 10.93 5.89 0.000
–––––––––––––––––––––––––––––––––––––––––––––––––
–––––––––––––––––––––––––––––––––––––––––––––––––
S = 749.394 R–Sq = 69.0% R–Sq(adj) = 63.0%
–––––––––––––––––––––––––––––––––––––––––––––––––

–––––––––––––––––––––––––––––––––––––––––––––––––
Analysis of Variance
–––––––––––––––––––––––––––––––––––––––––––––––––
–––––––––––––––––––––––––––––––––––––––––––––––––
Source DF SS MS F P
–––––––––––––––––––––––––––––––––––––––––––––––––
Regression 8 51341130 6417641 11.43 0.000
–––––––––––––––––––––––––––––––––––––––––––––––––
Residual Error 41 23025269 561592
–––––––––––––––––––––––––––––––––––––––––––––––––
Total 49 74366399
The regression analysis was initially run using all variables to determine the significance of each when associated

Statistics Chap12, Cases
Chapter 12 Simple Linear Regression Case Problem 1: Measuring Stock Market Risk a.Selected descriptive statistics follow: Variable N Mean StDev
Minimum Median Maximum Microsoft 36 0.00503 0.04537 –0.08201 0.00400 0.08883 Exxon Mobil 36 0.01664 0.05534 –0.11646 0.01279 0.23217
Caterpillar 36 0.03010 0.06860 –0.10060 0.04080 0.21850 Johnson & Johnson 36 0.00530 0.03487 –0.05917 –0.00148 0.10334 McDonald's 36
0.02450 0.06810 –0.11440 0.03700 0.18260 Sandisk 36 0.06930 0.19540 –0.28330 0.07410 0.50170 Qualcomm 36 0.02840 0.08620 –0.12170
0.03870 0.21060 Procter & Gamble 36 0.01059... Show more content on Helpwriting.net ...
PERCENT FATAL Fit Stdev.Fit Residual St.Resid 15 10.0 0.0390 1.2731 0.1126–1.2341 –2.13R 23 8.0 2.1900 0.6990 0.1548 1.4910 2.62R R denotes
an obs. with a large st. resid. There is a significant relationship between the two variables. Two observations are identified as having a large
standardized residual and should be treated as possible outliers; the following standardized residual plot does not indicate any other problems with
the residuals. [pic] Conclusion: It appears that the number of fatal accidents per 1000 licenses is linearly related to the percentage of licensed
drivers under the age of 21; that is, the higher the percentage of drivers under 21, the larger the number of total accidents. Case Problem 3: Alumni
Giving 1. Numerical and graphical summaries of the data follow. Variable N Mean Median TrMean StDev SE Mean Under 20 48 55.73 59.50 56.02
13.19 1.90 S/FRatio 48 11.542 10.500

Consume Research Inc
"Income ($1000s)""Household Size""Amount Charged
($)"6232,9216424,6032234,2732943,0673923,0743512,9213944,6035434,2732363,0672723,0742674,8206125,1493022,4772242,5144654,2146644,9654
... Show more content on Helpwriting.net ...
Residual analysis also shows no particular pattern and no problems of autocorrelation.Household Size explains about 40% of the variation in Amount
Charged.The Standard Error of the Estimate is a quite significant portion of the possible predicted values within the range: it is about 16% of the mode,
and 33% of the minimum. This indicates that the error in the prediction using this regression equation may be high, and I would consider this model
unacceptable if I were the client of Consumer Research, Inc. Annual Income is a slightlybetter predictor of Amount Charged, since it does explain
about 40% of the variationin Amount Charged, against only about 36% for Household Size.SUMMARY OUTPUTRegression StatisticsMultiple
R0.862318156R Square0.743592603Adjusted R Square0.73268165Standard Error476.1315166Observations50ANOVAdfSSMSFSignificance
FRegression230899839.8315449919.9168.151022021.28786E–14Residual4710654957.39226701.2211Total49

Linear Regression
Chapter 4
Multiple Linear Regression
Section 4.1
The Model and Assumptions
Objectives
Participants will: пЃ® understand the elements of the model пЃ® understand the major assumptions of doing a regression analysis пЃ® learn how to
verify the assumptions пЃ® understand a median split
3
The Model y пЂЅ пЃў o пЂ« пЃў1x1 пЂ« ... пЂ« пЃў p x p пЂ« пЃҐ or in Matrix Notation
Dependent Variable nx1 Unknown Parameters (p+1) x 1
Y пЂЅ XпЃў пЂ«e
Independent Variables– n x(p+1)
Error – nx1
4
Questions
How many unknown parameters are there? Can you name them? How many populations will be sampled? What are conceptual populations?
5

Major Requirements for Doing a Regression Analysis
The errors are normally distributed (not Y). Constant ... Show more content on Helpwriting.net ...
Problems if VIF > 10. Some people use the condition index. In order to avoid false positives, use the COLLINOINT option.
24
Variance Inflation Factor (VIF) Example
25
Collinearity Diagnostics – Not Adjusted
26
Collinearity Diagnostics – Adjusted
27
Body Fat Example
Variables
пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® 28
Percent body fat from Siri's (1956) equation – dependent Age (years) Weight (lbs) Height (inches) Neck circumference (cm) Chest circumference (cm)
Abdomen 2 circumference (cm) Hip circumference (cm) Thigh circumference (cm Knee circumference (cm) Ankle circumference (cm) Biceps
(extended) circumference (cm) Forearm circumference (cm) Wrist circumference (cm)
What Is Being Tested by |t|
30
continued...
What Is Being Tested by Pr >|t|
31

Partial F–Tests
H o : пЃў3 пЂЅ 0 | all other пЃў 's are in the model
32
Interpretation – The Stable Table
Do I need this leg to have a stable table?
Nope!
33
...
Nope!
34
...
Nope!
35

...
Graphs
Predicted versus Y Residual versus Independents Student versus Independents Cook's D versus Weight Leverage versus Weight
36
Moral of the Story
пЃ®
Removing more than one variable at a time is a

Introduction to Linear Regression and Correlation Analysis
Introduction to Linear Regression and Correlation Analysis
Goals
After this, you should be able to:
Calculate and interpret the simple correlation between two variables
Determine whether the correlation is significant Calculate and interpret the simple linear regression equation for a set of data Understand the
assumptions behind regression analysis Determine whether a regression model is significant
Goals
(continued)
After this, you should be able to:
Calculate and interpret confidence intervals for the regression coefficients Recognize regression analysis applications for purposes of prediction and
description Recognize some potential problems if regression analysis is used incorrectly Recognize ... Show more content on Helpwriting.net ...
sed to:
– Predict the value of a dependent variable based on the value of at least one independent variable – Explain the impact of changes in an independent
variable on the dependent variable
Dependent variable: the variable we wish to explain Independent variable: the variable used
Simple Linear Regression Model
Only one independent variable, x
Relationship between x and y is described by a linear function
Changes in y are assumed to be caused by changes in x

Types of Regression Models
Positive Linear Relationship Relationship NOT Linear
Negative Linear Relationship
No Relationship
Population Linear Regression
The population regression model:
Population y intercept Dependent Variable
Population Slope Coefficient
Independent Variable
y пЂЅ ОІ0 пЂ« ОІ1x пЂ« Оµ
Linear component
Random Error term, or residual
Random Error component
Linear Regression Assumptions
Error values (Оµ) are statistically independent Error values are normally distributed for any given value of x
The probability distribution of the errors is normal
The probability distribution of the errors has constant variance The underlying relationship between the x
Population Linear Regression y Observed Value of y for xi
y пЂЅ ОІ0 пЂ« ОІ1x пЂ« Оµ Оµi (continued)
Slope = ОІ1 Random Error for this x value

Predicted Value of y for xi Intercept = ОІ0
xi
x
Estimated Regression Model
The sample regression line provides an estimate of the

Computer Exercises Econometrics
Computer Exercises C1.2 Use the data in BWGHT.RAW to answer this question. . summ Variable | Obs Mean Std. Dev. Min Max
–––––––––––––+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– faminc | 1388 29.02666 18.73928
.5 65 cigtax | 1388 19.55295 7.795598 2 38 cigprice | 1388 130.559 10.24448 103.8 152.5 bwght | 1388 118.6996 20.35396 23 271 fatheduc | 1192
13.18624 2.745985 1 18 –––––––––––––+––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
motheduc | 1387... Show more content on Helpwriting.net ...
Std. Err. t P>|t| [95% Conf. Interval]
–––––––––––––+––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
lsales | .1621283 .0396703 4.090.000 .0838315 .2404252 lmktval |.106708 .050124 2.130.035 .0077787 .2056372 _cons | 4.620917 .2544083
18.160.000 4.118794 5.123041 log(salary)=Bo+B1log(sales)+B2log(marketvalue)+u
log(salary)=(0.1621283)log(sales)+(0.106708)(log(marketvalue)+4.620917 (ii) Add profits to the model from part (i). . regress lsalary lsales lmktval
profits Source |SS df MS Number of obs = 177 –––––––––––––+––––––––––––––––––––––––––––––––––––––––––––––––––––––––––F( 3, 173) =
24.64 Model | 19.3509799 3 6.45032663 Prob > F = 0.0000 Residual | 45.2952332 173 .261822157 R–squared = 0.2993
–––––––––––––+––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Adj R–squared = 0.2872 Total | 64.6462131 176 .367308029
Root MSE = .51169

Making Decisions Based on Demands
Making Decisions Based on Demand and Forecasting Latonya Woodrow Instructor Name: Dr.Samuel F. Onipede ECO 550–Managerial Economics
and Globalization July 21, 2013
College Students buy pizza in large quantities for a cheap price, but if the prices were to increase, then these same students may look for similar
alternatives that will not empty their wallets. These are possible alternatives that offer a large quantity of food at a reasonable price that can affect the
demand of pizza. However, monitoring the costs of the competing fast food restaurants in the ... Show more content on Helpwriting.net ...
The r–squared tells us that we have explained 72% in the regression of what will affect demand for a new pizza business to be profitable in the new
area. The position coefficient shows that as the independent variable changes the quantity demanded changes in the same direction.
Using the data collected on Morehead City in another example:
| Domino's Pizza = Growth Forecast based on Pizza Price | | | | | | |
| | | | | | | | |
Regression Statistics | | | | | | | |
Multiple R | 0.996320672 | | | | | | | |
R Square | 0.992654881 | | | | | | | |
Adjusted R Square | 0.985309761 | | | | | | | |
Standard Error | 328.6398738 | | | | | | | |
Observations | 3 | | | | | | | |
| | | | | | | | |
ANOVA | | | | | | | | | | df | SS | MS | F | Significance F | | | |
Regression | 1 | 14596204.5 | 14596204.5 | 135.144828 | 0.054627666 | | | |
Residual | 1 | 108004.1667 | 108004.167 | | | | | |
Total | 2 | 14704208.67 | | | | | | |
| | | | | | | | | | Coefficients | Standard Error | t Stat | P–value |

Forecasting By Vector Autoregression Models
CHAPTER NINE
FORECASTING BY VECTOR AUTOREGRESSION MODELS
9.1 Vector Autoregressive (VAR) Models
Vector Autoregression (VAR) models were introduced by the macro
– econometrician Christopher Sims (1980) to model the joint dynamics and causal
relations among a set of macroeconomic variables. The vector autoregression (VAR) is commonly used for forecasting systems of interrelatedtime
series and for analyzing the dynamic impact of random disturbances on the system of variables. The VAR approach sidesteps the need for structural
modeling by treating every endogenous variable in the system as a function of the lagged values of all of the endogenous variables in the system.
The vector autoregression (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It
is a natural extension of the univariate autoregressive model to dynamic multivariate time series. The VAR model has proven to be especially useful
for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from
univariate time series models and elaborate theory–based simultaneous equations models. Forecasts from VAR models are quite flexible because they
can be made conditional on the potential future paths of specified variables in the model.
In addition to data description and forecasting, the VAR model is also used for structural inference and policy analysis. In structural

Statistics
Annex A Basic Analysis | | Reciprocating| Scroll| All| Average Price| Europe| $ 31,31 | $ 38,60 | $ 32,28 | | Latin| $ 38,71 | Does not exist| $ 38,71 | |
North| $ 32,43 | $ 34,69 | $ 33,11 | | Total| $ 33,73 | $ 35,67 | $ 34,08 | Average Volume| Europe| 111.307,69| 88.000,00| 108.200,00| | Latin| 67.000,00|
Does not exist| 67.000,00| | North| 121.142,86| 174.500,00| 137.150,00| | Total| 103.054,05| 152.875,00| 111.911,11| Average BTU| Europe| 410,00|
850,00| 468,67| | Latin|... Show more content on Helpwriting.net ...
Annex D2 Residual plots of the first model
Annex E1 Regression analysis, second model
Except EER
Regression Analysis: Price/ Unit versus Capacity BTU/Hr; lnVolume; ...
* Europe is highly correlated with other X variables
* Europe has been removed from the equation.
* Scroll is highly correlated with other X variables
* Scroll has been removed from the equation.
The regression equation is
Price/ Unit = 44,7 + 0,00530 Capacity BTU/Hr – 1,19 lnVolume + 6,12 Latin– 0,237 North – 1,68 Reciprocating
Predictor Coef SE Coef T P VIF
Constant 44,698 6,196 7,21 0,000
Capacity BTU/Hr 0,005298 0,002134 2,48 0,017 1,771 lnVolume–1,1921 0,5151 –2,31 0,026 1,056
Latin 6,119 1,119 5,47 0,000 1,384
North –0,2375 0,9495 –0,25 0,804 1,424
Reciprocating –1,681 1,371 –1,23 0,228 1,758

S = 2,65254 R–Sq = 61,9% R–Sq(adj) = 57,0%
Source DF SS MS F P
Regression 5 446,245 89,249 12,68 0,000
Residual Error 39 274,403 7,036
Total 44 720,649
Source DF Seq SS
Capacity BTU/Hr 1 75,380

Assignment 1: Making Decisions Based on Demand and Forecasting
Managerial Economics and Globalization, ECO550
A market demand analysis is used to help understand how much consumer demand there is for a given product or service. This type of analysis will
help determine if a business can successfully enter a market and generate enough revenue and profit to maintain the business. One must identify the
market and the growth potential. Domino's Pizza was incorporated in 1963 and has been franchising since 1967. A traditional Domino's store is
located in shopping centers and/or strip malls with appropriate parking for delivery vehicles and walk–in customers for ... Show more content on
Helpwriting.net ...
The position coefficient shows that as the independent variable changes the quantity demanded changes in the same direction. Using the data
collected on Morehead City in another example: | Domino's Pizza = Growth Forecast based on Pizza Price| | | | | | | | | | | | | | | | Regression Statistics| | | | | |
| | Multiple R| 0.996320672| | | | | | | | R Square| 0.992654881| | | | | | | | Adjusted R Square| 0.985309761| | | | | | | | Standard Error| 328.6398738| | | | | | | |
Observations| 3| | | | | | | | | | | | | | | | | ANOVA| | | | | | | | | | df| SS| MS| F| Significance F| | | | Regression| 1| 14596204.5| 14596204.5| 135.144828|
0.054627666| | | | Residual| 1| 108004.1667| 108004.167| | | | | | Total| 2| 14704208.67| | | | | | | | | | | | | | | | | Coefficients| Standard Error| t Stat| P–value|
Lower 95%| Upper 95%| Lower 95.0%| Upper 95.0%| Intercept| 28020.81833| 1635.404715| 17.133874| 0.03711352| 7241.031202| 48800.6055|
7241.0312| 48800.6055| X Variable 1|–2701.5| 232.3834833| –11.6251808| 0.05462767| –5654.212117| 251.212117| –5654.21212| 251.212117| | | | | | |
| | | | | | | | | | | | | | | | | | | | | | RESIDUAL OUTPUT| | | | | |

Case 49
Descriptive Statistics for Crimes
Variable N N* Mean SE Mean StDev Minimum Median Maximum Range Mode
CRIMES 50 0 4559 174 1232 2107 4366 7820 5713 5705.7
N for
Variable Mode
CRIMES 2
[pic]
[pic]
[pic]
[pic]
[pic]
[pic]
[pic]
One–Sample Z
The assumed standard deviation = 1232

N Mean SE Mean 95% CI
50 4559 174 (4218, 4900)
One–Sample T
N Mean StDev SE Mean 95% CI
50 4559 1232 174 (4209, 4909)
[pic]
[pic]
Regression Analysis: CRIMES versus URBAN
CRIMES = 737.0 + 57.18 URBAN
S = 917.074 R–Sq = 45.7% R–Sq(adj) = 44.6%
Source DF ... Show more content on Helpwriting.net ...
Regression Analysis: CRIMES versus UNEMPLOY
CRIMES = 4364 + 35.5 UNEMPLOY
Predictor Coef SE Coef T P
Constant 4364.2 539.8 8.08 0.000
UNEMPLOY 35.55 93.00 0.38 0.704
S = 1242.82 R–Sq = 0.3% R–Sq(adj) = 0.0%

Source DF SS MS F P
Regression 1 225660 225660 0.15 0.704
Residual Error 48 74140739 1544599
Total 49 74366399
Unusual Observations
Obs UNEMPLOY CRIMES Fit SE Fit Residual St Resid 9 5.0 7820 4542 182 3278 2.67R 18 10.9 5043 4752 533 292 0.26 X 43 7.3 7365 4624 243
2741 2.25R 48 9.9 2107 4716 446 –2609 –2.25RX
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large

Pt1420 Unit 4 Paper
6.Technical Findings 6.1 Results of 10–year data Table 6.1.1 displays the matlab output of beta, standard error, t–statistic and p–value for the two
independent variables during 10–year period. It is found that beta of X1 is 0.2750 which indicates there is a positive relationship between the utilities
excess return and the healthcare excess return. This positive relationship is statistically significant as the p–value is close to 0 which is much less than
the significance level of 5%. In addition, the standard error of X1 is 0.0300 which represents the average distance that the observed values fall from the
regression line. This indicates that the model fits the data. In contrast, it is found that the material excess return is negatively... Show more content on
Helpwriting.net ...
And the outputs of both dataset are consisted with the results applying the robust function. Betas stay the same and only the standard errors in both
cases decline slightly. Similarly, the graphs in both cases remain the same. Based on the non–robust test, we then can confirm that the variables are
independent and uncorrelated, which satisfies the easing assumption to state the model is consistent. 7.Criticism Referring to Figure 6.2.3 and 6.3.3,
it was proven that our model has problems that the sample data used does not represent the whole population. Therefore, this is one of the flaws in
our research. A more constructive suggestion to eliminate this problem would be to extend the research with a larger sample size with longer time
horizon. And if the sample size is large enough, the time series issue can be neglected. Another issue might arise by the determination of explanatory
variables. As we follow the facesheet from Morningstar (2011) to choose our explanatory variables as a symbol of the defensive sectors and cyclical
sectors, it is possible that the category of sectors is incorrect. Without the support of academic journals, the facesheet might be purely based on analysts
'opinions instead of facts. By estimating wrong explanatory variables, the findings will become

Case 28: Housing Prices
Case 28: Housing Prices
GM533 Managerial Statistics
April 11, 2012
I'm conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home's price. The data is analyzed using
ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. Theregression analysis will help create a multiple regression fit
which will incorporate the ten predictor variables of a home's price. After the regression analysis is complete, global and local ANOVA tests will help
eliminate the insignificant predictor variables and create the net significant regression equation. Even though the sample size is only representative of
the houses in Oregon, the general trends that affect house prices are ... Show more content on Helpwriting.net ...
Calculate test statistic 4. Compare with the decision rule 5. Determine the final decision
The hypotheses for this test are:
Null hypothesis:b1–10=0
Alternative hypothesis:b1 and or 2 and or 3 and or 4...and or 10в‰ 0
The null hypothesis consists of all independent variables that have a regression coefficient which is equal to zero; the alternative hypothesis is at least
one independent variable that has a regression coefficient which is not zero.
The rejection of the hypotheses will be done via the traditional method. In this method, the test statistic is calculated and then the p–value is computed
as well. The p–value is compared with the significance level of 0.05, and then the decision is made whether to reject the null hypothesis or not. The test
statistic calculated from Minitab is about 45.91 shown in the following information:
PRICE = – 15.2 + 0.0376 SQFT + 4.92 BEDS – 2.91 BATHS– 12.9 HEAT + 2.29 STYLE + 15.8 GARAGE + 9.08 BASEMENT– 1.03 AGE + 5.31
FIRE + 4.62 SCHOOL
Predictor Coef SE Coef T P
Constant –15.212 9.818 –1.55 0.125
SQFT 0.037596 0.003627 10.36 0.000
BEDS 4.924 1.965 2.51 0.014

BATHS –2.912 3.024 –0.96 0.338
HEAT–12.910 6.101 –2.12 0.037
STYLE 2.288 1.644 1.39 0.167
GARAGE 15.759 3.825 4.12 0.000
BASEMENT 9.077 3.445 2.63 0.010
AGE –1.0342 0.2813 –3.68

Forecasting Using Eviews
Data The variables of interest are oil imports to Germany, and temperature in Germany. The latter is used as a leading indicator for the former, to
improve on the forecast obtained by the univariate model. Both variables are collected over a time range from January 1985 until and including
December 1997, whereas the last year is not used for constructing the optimal forecast, obtained by fitting a model through the data until the end of
1996. This will enable us to forecast the year 1997 using our model, and then comparing it to the actual data. Assuming no large one time shock,
meaning that it is not captured by seasonality or cyclical behaviour in the data, occurs in this year, a graphical comparison of our forecast and the
whole data... Show more content on Helpwriting.net ...
The insignificant December month can be explained by the little temperature difference compared to the base month January, and roughly the same oil
is consumed therefore. Moreover the negative signs of their coefficients are in line with intuition, that in the coldest month January more oil is needed
than in all the other month. The last step before fitting autoregressive and moving averages terms to the data, is to check for unit roots. We will use the
augmented Dickey–Fuller test to decide whether the data has a unit root or not. The H0 of the test is that the data has a unit root against the Ha, that
the data has no unit root. Table 1.1 shows the result of the augmented Dickey–Fuller test. The p–value of the

Using Multiple Regression Project For New Stores
Multiple Regression Project Jay Ma MGSC6200, 50839 Facilitator: Ike Papadopoulos 05JUL2015 Introduction Pam and Susan's is a discount
department store that currently has 250 chain stores located throughout the South. To sustain its recent continuous expansion into the Border States
and Southwest, identification of the sites for new stores has become an essential factor of its increasing strategic planning. The current store
decisions are based on the traditional process of estimating sales potential with demographic analyses, site visits and studies by the company's real
estate department. A subjective approach was developed to classify the potential sites to the "competitive type" of the trading zone. However, there
are ongoing concerns regarding the subjectivity of the "competitive type" classifications, and whether this method can predict sales potentials at new
sites. The goal of this project is to build a multiple regression model based on the census data obtained from stores' trading zones and individual
stores, to effectively predict sales potentials at new sites. Data The gathered data contains demographics and economics of the trading zones, and size,
composition and sales of the store. There is a total of 13 major categories of variables including population (%black, % Spanish speaking), total
population , family income (in $1,000s: 0–10, 10–14, 14–20, 20–30, 30–50, 50–100, >100), average family size, median yearly income, median rent
per month, median home

Business Forecasting
Content Introduction1 Part 1. Examine the data, looking for seasonal effects, trends and cycles2 Part2. Dummy Variables Model3 Linear trend model3
Quadratic trend model5 Cubic trend model7 Part 3. Decomposition and Box–Jenkins ARIMA approaches8 First difference:10 a. Create an ARIMA (4,
1, 0) model10 b. Create an ARIMA (0, 1, 4) model11 c. Create an ARIMA (4, 1, 4)11 d. Model overfitting12 Second difference13 Forecast based on
ARIMA (0, 1, 4) model13 Return the seasonal factors for forecasting14 Part 4. Discussion of different methods and the results15 Comparison of
different methods in terms of time series plot15 Comparison of different models in terms of error17 Assumptions and the... Show more content on
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Therefore, this linear model is not good and it may be enhanced by non–linear models. Quadratic trend model A new dummy variable TIME2 is created
in this model (TIME2= TIME*TIME). The equation of this model is: Data=a+ c1 time +c2 (time) 2 + b1Q1+b2Q2+b3Q3+ error The regression
model is built up with Stepwise method as well, and the output is simplified and only the useful model is left. The significance of Q2 and Q3 is over
0.05 through F–test therefore being removed from the model. The adjusted R square is 97% which shows a good fit and better than the linear model.
To build the Quadratic trend model according to the output: Trend–cycle = 11698.512 + 1297.080*TIME – 9.143* TIME2 – 1504.980* Q1 + error
Model Summaryd| Model| R| R Square| Adjusted R Square| Std. Error of the Estimate| 3| .986c| .971| .970| 2275.62420| a. Predictors: (Constant),
TIME, TIME2, Q1b. Dependent Variable: creditlending| Coefficientsa| Model| Unstandardized Coefficients| Standardized Coefficients| t| Sig.| | B| Std.
Error| Beta| | | 3| (Constant)| 11698.512| 946.957| | 12.354| .000| | TIME| 1297.080| 74.568| 1.643| 17.395| .000| | TIME2| –9.143| 1.246| –.693| –7.338|
.000| | Q1| –1504.980| 700.832| –.050| –2.147| .036| a. Dependent Variable: creditlending| As you can see on the sequence chart displayed above, this
model is not very good as well. First of all, the model fit the modelling data

Reportfinal Essay
Course ADVANCED ECONOMETRICS ProgrammeMSc in Finance Site HEC Lausanne Semester Fall 2014 Module LeaderDiane Pierret Teaching
AssistantDaria Kalyaeva Assessment Type: Empirical Assignment Assessment Title:A Dynamic Model for Switzerland GDP Written by:Group Y
(Ariane Kesrewani & Alan Lucero) Additional attachments: Zip Folder containing Matlab code, data and figures. Submission Date: December 15 at
00.05 1. Descriptive Statistics a. Time series plots of GDP level and GDP growth i. Definition of weak stationarity. GDP level and growth stationarity.
A stochastic... Show more content on Helpwriting.net ...
ii. Observations from plots. As mentioned before, we can observe from the plots that the GDP level is upward trending, which is a characteristic
feature of economic time series. To offset this, we calculate the first differences as a change in logs. Once plotting the vector of the results, another
characteristic of economic time series arises in the plot of GDP growth: seasonality. This can be seen in quarterly variations year on year, for
example quarter four of each year cannot be purely compared to quarter two since it accounts for a big holiday variation such as Christmas spending,
end of year boosting of financial results, etc. Thus growth should be assessed with the corresponding quarter year on year. This effect compensates the
business cycles variations which are more significant for

Computer Exercises Econometrics
Computer Exercises C1.2 Use the data in BWGHT.RAW to answer this question. . summ Variable | Obs Mean Std. Dev. Min Max
–––––––––––––+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– faminc | 1388 29.02666 18.73928
.5 65 cigtax | 1388 19.55295 7.795598 2 38 cigprice | 1388 130.559 10.24448 103.8 152.5 bwght | 1388 118.6996 20.35396 23 271 fatheduc | 1192
13.18624 2.745985 1 18 –––––––––––––+––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
motheduc |... Show more content on Helpwriting.net ...
Average salary= $865,864.40 Average tenure with company= 22.50282 years Average tenure as CEO=7.954802 (ii) How many CEO's are in their first
year as CEO (that is, ceoten=0)? CEO's in their first year as CEO=5 What is the longest tenure as a CEO? Longest tenure as a CEO= 37 years (iii)
Estimate the simple regression model log(salary)=Bo+B1ceoten+u and report your results in the usual form. What is the (approx) predicted percentage
increase in salary given one more year as a CEO? Source | SS df MS Number of obs = 177

A Successful Analysis Of Data Using Ancova
A successful analysis of data using ANCOVA is dependent on assumptions that relate to the study design, namely, a continuous dependent variable, a
categorical independent variable with two or more independent groups, a continuous covariate variable, and the need for independence of observation
(Field, 2012). Other assumptions are presented during the course of the SPSS analysis to ensure data does not violate assumptions, even though there
is some leeway for violation of certain assumptions. For the analysis, time spent stalking after therapy (stalker2) was the dependent variable, the
intervention group (cognitive behavior therapy contrasted with psychodynamic therapy) was the independent variable, and time spent before therapy
(stalker1) was the covariate to control for incoming behavior. Two study designs are associated with ANCOVA– pre–post study designs and
condensing the outcome of inappropriate variable, but only after the preliminary assumptions have been satisfied (Huitema, 2011). The Stalker.sav data
was appropriate for measuring the same dependable variable in two or more unrelated independent groups over two points; hence the pre–post study
design. Linearity Assumption There was a linear relationship between the time spent stalking before therapy (pre) and the time spent stalking after
therapy (post) for each intervention type, as evidenced by the scatterplot below. Testing for Normality From the Tests of Normality table below, the
Shapiro–Wilk

Model Reuse With Bike Rental Station

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