Temporal Variation Of Municipal Water Quality

Temporal Variation Of Municipal Water Quality
Spatio–Temporal Variation in Municipal Water Quality in Abuja, Nigeria 1Abiola Kassim
Abayomi¹*, Olanrewaju Lawal² and Medugu Nasiru Idris3 ¹and 3 Department of Geography,
Faculty of Social Sciences, Nasarawa State University, Keffi, Nigeria 2 Department of Geography
and Environmental Management, Faculty of Social Sciences, University of Port Harcourt, P.M.B
5323, Choba Campus, Port Harcourt. *kassima2013@gmail.com Abstract A total number of Eighty
eight water samples were collected at different designated point areas in the area councils of FCT,
Abuja. The qualities of the samples were analyzed for the physico–chemical properties of water
supplied from difference sources in the council areas. However fourteen parameters were
determined in the water samples supplied to these areas, using appropriate physical and chemical
laboratory technics. The results of the physico–chemical analyses indicated variation in the amount
elements (eg. pH, TDS, Colour, BOD. Anion and cations) that are present in the water consumed
and supplied. Significant positive correlation was observed between and among the parameter at
0.05significant level (Kruskal–Wallis Statistical Technique).Furthermore Moran's I was computed to
examined global spatial autocorrelation. In addition to this spatial autocorrelation analysis, local
clustering of the values was also examined using Hot Spot Analysis (Getis–OrdGi*) revealed point
that are statistically significant hot or cold spot across the area sample. One
... Get more on HelpWriting.net ...

Reportfinal Essay
Course ADVANCED ECONOMETRICS Programme MSc in Finance Site HEC Lausanne Semester
Fall 2014 Module Leader Diane Pierret Teaching Assistant Daria 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

Empirical Results From The Modeling Of Claim Inflation
4.2 ARIMA MODEL
This chapter displays the empirical results from the modeling of claim inflation using ARIMA
model.
Data Description
Series=claim inflation
Sample 1984–2014
Observations=30
Mean=2.748
Median=2.415
Minimum=1.25
Maximum=7.15
Standard deviation=1.43012
Kurtosis=1.679
Skewness=1.354
4.2.1 Descriptive Statistics for the claim inflation series
The data is not stationary since it does not exhibit a certain state of statistical equilibrium showing
that the variance changes with time. Performing a log transformation still produces a non–stationary
process in which case we should difference the series before continuing.
ACF and PACF
4.2.2 Unit Root Test for CPI Series
Test for unity we use the ADF test for unit test hypothesis;
Ho: the CPI has unit root (non–stationary) Vs H1: CPI data has no unit root (stationary). Augmented
Dickey fuller test
Data: log.claiminf
Dickey–fuller = –9.6336 lag order=12 p–value=0.01
Alternative hypothesis stationary warning message:
4.2.3 Model Identification, Estimation and Interpretation
ARIMA models are univariate models that consist of an autoregressive polynomial, an order of
integration (d), and a moving average polynomial. Since Claim inflation became stationary after
first order difference (ADF test) the model that we are looking at is ARIMA (p, 1, q). We have to

identify the model, estimate suitable parameters, perform diagnostics for residuals and finally
forecast the inflation series.

Econ
MULTIPLE CHOICE (CHAPTER 4) 1. Using a sample of 100 consumers, a double–log regression
model was used to estimate demand for gasoline. Standard errors of the coefficients appear in the
parentheses below the coefficients. Ln Q = 2.45 –0.67 Ln P + . 45 Ln Y – .34 Ln Pcars (.20) (.10)
(.25) Where Q is gallons demanded, P is price per gallon, Y is disposable income, and Pcars is a
price index for cars. Based on this information, which is NOT correct? a. Gasoline is inelastic. b.
Gasoline is a normal good. c. Cars and gasoline appear to be mild complements. d. The coefficient
on the price of cars (Pcars) is insignificant. e. All of the coefficients are insignificant. 2. In a ... Show
more content on Helpwriting.net ...
a, b, and c 12. The estimated slope coefficient (b) of the regression equation (Ln Y = a + b Ln X)
measures the ____ change in Y for a one ____ change in X. a. percentage, unit b. percentage,
percent c. unit, unit d. unit, percent e. none of the above 13. The standard deviation of the error
terms in an estimated regression equation is known as: a. coefficient of determination b. correlation
coefficient c. Durbin–Watson statistic d. standard error of the estimate e. none of the above 14. In
testing whether each individual independent variables (Xs) in a multiple regression equation is
statistically significant in explaining the dependent variable (Y), one uses the: a. F–test b. Durbin–
Watson test c. t–test d. z–test e. none of the above 15. One commonly used test in checking for the
presence of autocorrelation when working with time series data is the ____. a. F–test b. Durbin–
Watson test c. t–test d. z–test e. none of the above 16. The method which can give some information
in estimating demand of a product that hasn't yet come to market is: a. the consumer survey b.
market experimentation c. a statistical demand analysis d. plotting the data e. the barometric method
17. Demand functions in the multiplicative form are most common for all of the following reasons
except: a. elasticities are constant over a range of data b. ease of estimation of elasticities

Is Walmart Safe?
Is Walmart Safe?
The Effects of Established Supercenter Walmarts to Property Crime Rates within Dekalb and
Gwinnett County from 1999–2010
Class: Economics & Finance Modeling
Professor: Doctor Derek Tittle
Dream Team Group Members:
Alexandra E Steingaszner
Brian–Paul Gude
Kristopher Bryant
Norman Gyamfi
Samantha Gowdy
|
Disclaimer
This report has been created in the framework of a student group project and the Georgia Institute of
Technology does not officially sanction its content.
Executive Summary
Every year, Walmart is accused of increasing crime in areas within which it builds Walmart
Supercenters. Yet, research and data analyses largely disprove these claims, as they reveal that other
factors such as ... Show more content on Helpwriting.net ...
Iterations of analysis eliminated data points that were listed as "unusual observations," or any data
point with a large standardized residual. After 5 iterations, the analysis showed improved residual
plots. Randomness in the versus fits and versus order plots means that the linear regression model is
appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the
data, and a bell shaped curve in the histogram illustrates the normality of the data.
Because of the method of monthly data collection, absolute randomness could not be obtained;
however, it was decided that 5 iterations was sufficient because the sixth iteration showed a decrease
in the quality of the residual plots. The first test performed was the p–value test of the individual
variables. A p–value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the
one that was actually observed. The only input that did not have a p–value less than 0.05, which was
the chosen significance level, was the "Number of Walmarts" variable; the number of Walmarts has
no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of

determination, provides a measure of how well future outcomes are likely to be predicted by the
model. R2 values range from 0 to 100% (or 0 and 1) and the

A Brief Note On Diabetes Prevalence Rate And Socioeconomic...
Diabetes is a major health problem in the United States. There is an increasing interest in the
relationship between diabetes and sociodemographic and lifestyle factors but the extent of the
geographical variability of diabetes with respect to these variables still remains unclear. The
regression models commonly used for disease modeling either use Ordinary Least Square (OLS)
regression by assuming all the explanatory variables have the same effect over geographical
locations or Geographically Weighted Regression (GWR) that assumes the effect of all the
explanatory variables vary over the geographical space. In reality, the effect of some of the variables
may be fixed (global) and other variables vary spatially (local). For this type of ... Show more
content on Helpwriting.net ...
Diabetes is associated with obesity, physical inactivity, race and other socioeconomic covariates
(Hipp & Chalise, 2015). There is a steady increase in type 2 diabetes prevalence especially in
adolescents and African Americans (Arslanian, 2000; Arslanian, Bacha, Saad, & Gungor, 2005;
Harris, 2001).
Studies of the correlates of diabetes ignore the spatial non–stationarity by either fitting OLS method
or using all the variables as nonstationary by fitting GWR model. A number of studies (Chen, Wu,
Yang, & Su, 2010; Dijkstra et al., 2013; Hipp & Chalise, 2015; Siordia, Saenz, & Tom, 2012) used
GWR model to study the association between diabetes and other covariates.
GWR is one of the localized regression techniques which accounts for spatial heterogeneity or
spatial non– stationarity (Benson, Chamberlin, & Rhinehart, 2005; C. Brunsdon, Fotheringham, &
Charlton, 1996; Fotheringham, Brunsdon, & Charlton, 2003; Lu, Harris, Charlton, & Brunsdon,
2015). As an exploratory tool, GWR is useful in wide varieties of research fields including but not
limited to health and disease (Chalkias et al., 2013; Chen et al., 2010; Chi, Grigsby–Toussaint,
Bradford, & Choi, 2013; Dijkstra et al., 2013; Fraser, Clarke, Cade, & Edwards, 2012; Hipp &
Chalise, 2015; Lin & Wen, 2011; Nakaya, Fotheringham,

The Housing Bubble And The Gdp : A Correlation Perspective
LITERATURE REVIEW A study from Ray M. Valadez, "The housing bubble and the GDP: a
correlation perspective" in Journal of Case Research in Business and Economics has been done to
focus on the relationship between the Real Gross Domestic Product and the situation of Housing
Bubble. In this research, the author has concentrated on the time from the beginning of losing trust
in government from the financial institution. He emphasizes how much the housing bubble relates to
the recession in the economy. The author takes the sample on changes in GDP and changes in the
housing price index from 2005 to 2006 in order to illustrate the statistical connection between them.
The dependent variable were used is quarterly changes of adjusted GDP, the database of the research
were base on a report on NCSS software. According these results, the changes in both HPI and GDP
have likely similar common from in the period of 2005 and 2006, the data showed that there were
significant changes in the next two years. The result also showed that housing price and GDP has
been long observed and their relationship has more innovations at the end of 2009. Another
Research has done by a group of composers including Zhuo Chen, Seong–Hoon Cho, Neelam
Poudyal and Roland K. Roberts. The name of research was "Forecasting Housing Prices under
Different Submarket Assumptions." The paper focus on the submarket and use the data of home
sale. The database was taken from the Knoxville city combined with

What Is The Methodology Used In Costimating The Impact Of...
This section gives and explains the methodology that is going to be used in estimating the impact of
capital flight on economic growth in Zimbabwe for the period 1980 to 2015. This encompasses the
specification of the model but no specific theory can be attributed to the selection of variables to be
used. Model diagnostic tests are to be conducted before interpretation of estimated results of the
correctly specified model. 3.0Methodology There are quite number of methods of estimating
regression functions, the generally used ones being the ordinary least squares (OLS) and the
maximum likelihood (ML). This paper will use (OLS) over (ML) because of the properties of (OLS)
that is its ability to produce best linear unbiased estimate thus ... Show more content on
Helpwriting.net ...
3.2 Stationarity Test Testing the stationary properties of time series is a very important exercise as
the use of stationary time series data in the Classical Linear Regression Model will result in inflated
results. The results are likely to be inconsistent and with a low Durbin Watson (DW) statistic.
Several methods can be employed to test whether the time series variables are stationary , these
includes residual plot but this paper will employ the Augmented Dickey Fuller (ADF) to test the
existence of a unit root. Conclusion of stationarity is going to be considered at 1% and 5% level of
significance only. Any probability of each variable below the two values will be considered
stationary. If the model fails to meet the stationary requirement, we will use the differencing method
to make our model stationary. 3.3 Model Diagnostic Tests Multicollinearity is a test to assess the
randomness of explanatory variables. They are other tests which include the Auxiliary Regressions
and correlation matrix. This study will consider pair wise correlation coefficient from the correlation
matrix. If the pair–wise or zero–order correlation coefficient between two explanatory variables is
high, say in excess of 0.8, then multicollinearity is a serious problem (Gujarati, 2004: 359). In the
case that two variables are highly correlated then one of it must be dropped. For

Statistical Analysis of Basketball Shooting in a...
When I watch basketball on television, it is a common occurrence to have an announcer state that
some player has the hot–hand. This raises the question: Are Bernoulli trials an adequate model for
the outcomes of successive shots in basketball? This paper addresses this question in a controlled
(practice) setting. A large simulation study examines the power of the tests that have appeared in the
literature as well as tests motivated by the work of Larkey, Smith, and Kadane (LSK). Three test
statistics for the null hypothesis of Bernoulli trials have been considered in the literature; one of
these, the runs test, is effective at detecting one–step autocorrelation, but poor at detecting
nonstationariy. A second test is ... Show more content on Helpwriting.net ...
Their third test is a test of fit and the researchers refer to it as a test of stationarity. The test is
nonstandard, but simple to describe. Suppose that the data are
1100100011110101 . . . .
Group the data into sets of four,
1100 1000 1111 0101 . . . , and count the number of successes in each set,
2, 1, 4, 2 . . . .
Use the 25 counts to test the null hypothesis that the data come from a binomial distribution with n =
4 and p estimated as the proportion of successes obtained in the data. The first difficulty with
implementing this test is that typically one or more of the expected counts is quite small. The
researchers overcame this problem by combining the O's and E's to yield three response categories:
fewer than 2, 2, and more than
2, and then applied a χ
2
test with one degree of freedom. The test can be made one–sided by rejecting if and only if the χ
2
test would reject at 0.10 and E > O for the middle category (corresponding to two successes). The
rationale for this decision rule is that E > O in the central category indicates heavier tails, which
implies more streakiness. The theoretical basis for this test is shaky, but the simulation study
reported in Section 3

Analysis Of The Bank Of Canada
With Canada's economy growing in every direction, we see a lot of new changes done by the Bank
of Canada; which can have vast affects on the economy and our standard of living. In this analysis I
look at three variables: the Bank Rates, Consumer Price Index (CPI), and Foreign Exchange Rates.
Before I get into the actual data I'd like to give a brief description on how each variable affect each
other. As we know interest rate and inflation have a negative relationship, meaning as one increase
the other decreases. The Bank of Canada tend to increase interest rates if they see that inflation is
starting to increase so they increase interest rates to reduce the inflation rate and vice versa.
However for exchange rates and interest rates the ... Show more content on Helpwriting.net ...
Empirical Analysis:
Considering the following regression model: BRi=β0++β1(Y)+β2(Z)+ui which connects the bank
rate (BR) of Canada to foreign exchange rates(Y) and CPI(Z). In this model X1 and X2 are the
corresponding independent variables exchange rates and CPI measured in decimals. There were
three estimation methods that were used to estimate the model: The Durbin Watson test is used to
test the presence of autocorrelation. The residual values from the regression analysis helps
determine if there is a relationship between values that are lagged. The result of the Durbin Watson
test lies between 0 and 4 and depending on the value it will show the presence or absence of
autocorrelation. The value that is closer to 0 indicates that there is positive autocorrelation, 2
indicates that there is no autocorrelation and values approaching 4 indicate that there is negative
autocorrelation. For the hypothesis testing I've used the F–Statistic testing, in the later section of the
paper I will explain my findings and the results.
The hypothesis test helps understand if the null hypothesis should be rejected or not. The purpose of
the F test is to estimate if there is a larger difference among the sum of square residuals. I used the
F–test to run my testing according to the data we conclude by rejecting the null hypothesis for both
tests, due to F–Statistic>F–Critical. Therefore in this case as bank rates

Regression Analysis of Dependent Variables
Table: 1, represents the results of regression analysis carried out with the dependent variables of
cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal,
cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as
CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination,
denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and
the adjusted R² values in the analysis are fairly good which is more than 60%, indicates the
considered model is fit for analysis. Also, the F–Statistics which provides the statistical significance
of the model and its probabilities which are below 5% level and hence proves the model's
significance.
Table: 1: Regression Results.
Method: Least Squares
Sample: 2005Q1 2013Q4
Included observations: 36
R–squared Adjusted R–squared F–statistic Prob(F–statistic)
0.955378 0.946146 103.4845 0.00000
0.963182 0.955564 126.4426 0.00000
0.746736 0.90889 15.58318 0.01877
0.952115 0.942208 96.10377 0.00000
0.960883 0.95279 118.7272 0.00000
0.868418 0.841194 31.89909 0.00000
0.87641 0.85084 34.27454 0.00000
0.933336 0.919543 67.66915 0.00000
0.889215 0.866294 38.79462 0.00000
0.924163 0.908473 58.89987 0.00000
0.739903 0.68609 13.74949 0.00000
Serial Correlation and Heteroskedasticity:
Normally the possibilities for the time series data to have the Serial correlation or auto correlation
are more. It can be tested with the

Model Of Ols Model
whether the independent variable had a positive or negative relationship to the dependent variable.
This was helpful when studying the graduated colours map of number of votes to determine how the
variables could help explain the patterns seen on the map. Once a variable was deemed suitable, an
OLS model was run to test the hypothesis that the number of votes is a function of the chosen
variable. This process was repeated with different groups of variables while assessing the outputs
and altering the composition of variables. The checks included ensuring that the coefficients have
the expected sign and are statistically significant, that there is no redundancy in the explanatory
variables, high adjusted R2 value, low AIC value and ... Show more content on Helpwriting.net ...
This model was chosen because it experienced the most significant increase in adjusted R2 (up from
90.5%) and a decrease in AIC (down from 773.9) from the OLS model. The coefficients that were
computed by the GWR tool and mapped (Figure 2) helped to demonstrate that each explanatory
variable and its associated coefficient vary spatially in its predictive strength of the dependent
variable. As we know, there is spatial autocorrelation and relationships in the data. This is not
necessarily negative but it is important to capture the structure of the correlation in the model
residues with explanatory variables. Until then, the model cannot necessarily be trusted (ESRI,
2016). However, the high level of significance of the p–value (0.0000) and the z–value (6.059441)
indicate that the model can be trusted. The small p–value indicates that the coefficients are not zero
and therefore the explanatory variables are statistically significant predictors of the behaviour of the
dependent variable (ESRI, 2016). The small dataset is more troubling. Some future solutions for
eliminating spatial autocorrelation include continuing to resample variables until there is no more
statistically significant spatial autocorrelation (clustering). Unfortunately, that was not accomplished
during the OLS regression without interfering with the ability of the GWR to run. The output of the
OLS

Econometric Essay
Table of Contents Chapter 1: INTRODUCTION 2 Chapter 2: THEORETICAL BASIS 3 Chapter 3:
DATA COLLECTION 5 Chapter 4: EMPIRICAL MODEL AND HYPOTHESIS TESTS 7 Chapter
5: CONCLUSION 14 Chapter 1: INTRODUCTION Since the introduction of doi moi (renovation)
economic reforms in 1986, Vietnam's economy has been among the fastest growing economies in
the region. Its economic structure reflected an increasing share of industry and services while the
share of agriculture declined. Vietnam has been successful in poverty reduction strategies and has
been able to ensure rapid growth with ... Show more content on Helpwriting.net ...
This Dummy variable includes: 0: mountain area and midland 1: coast 2: Delta Its expectation sign
is positive (+) Therefore, the model proposed is: FDI = [pic]INDUSTRIAL ZONE +[pic]SCHOOL
+ [pic]POLICY +[pic]DENSITY +[pic]REGION Chapter 3: DATA COLLECTION 3.1 Source of
survey: The data is collected from some websites of General Statistic Office as well as Industrial
Zones in Vietnam 3.2 Scope of survey: My group collected the data from 45 provinces in Vietnam
randomly, after that we classified them into 5 categories: population density, the number of
industrial zones, school, policy, and region 3.3 Data table: [pic]The estimated model 1 is: FDI = –
3023.01 + 757.328 INDUSTRIAL ZONE + 4.47475 SCHOOL + 2778.14 POLICY + 2.64933

Creating a Model to Forecast the Adjusted Close Price of...
Aim of the Project
My intention is to create a model in order to forecast the adjusted close price of Paddy Power PLC
shares. I will examine some of the different Statistical Modelling techniques and evaluate the merits
of each in turn.
I will use the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model if it is
found that the variance of the time series is non–constant. My final forecasting model will primarily
use the Autoregressive Integrated Moving Average (ARIMA) model to predict future closing prices
of the share, with a GARCH model of the variance incorporated.
I will use the R Software to implement these methods. R is a large open source statistical software
which is favoured by many professional statisticians and academics.
Data Set
I have obtained the Adjusted Daily Close Prices of Paddy Power PLC as quoted on the Irish Stock
Exchange for the past 3 years, from October 15th 2008 to October 13th 2011. I believe that a sample
of this size is large enough to test for statistical trends, such as seasonality. I have plotted my data set
using the R software package. Figure 1 is what was generated. A sample of the data can be found in
the References along with a link to an internet page containing the data.
Figure 1 Statistical Modelling Methods
Multiple Linear Regression
Regression analysis involves finding a relationship between a response variable and a number of
explanatory variables. For a sample number t, with p explanatory

Measuring A Computational Prediction Method For Fast And...
In general, the gap is broadening rapidly between the number of known protein sequences and the
number of known protein structural classes. To overcome this crisis, it is essential to develop a
computational prediction method for fast and precisely determining the protein structural class.
Based on the predicted secondary structure information, the protein structural classes are predicted.
To evaluate the performance of the proposed algorithm with the existing algorithms, four datasets,
namely 25PDB, 1189, D640 and FC699 are used. In this work, an Improved Support Vector
Machine (ISVM) is proposed to predict the protein structural classes. The comparison of results
indicates that Improved Support Vector Machine (ISVM) predicts more accurate protein structural
class than the existing algorithms.
Keywords–Protein structural class, Support Vector Machine (SVM), Naïve Bayes, Improved
Support Vector Machine (ISVM), 25PDB, 1189, D640 and FC699.
I. INTRODUCTION (HEADING 1)
Usually, the proteins are classified into one of the four structural classes such as, all–α, all–β, α+β,
α/β. So far, several algorithms and efforts have been made to deal with this problem. There are two
steps involved in predicting protein structural classes. They are, i) Protein feature representation and
ii) Design of algorithm for classification. In earlier studies, the protein sequence features can be
represented in different ways such as, Functional Domain Composition (Chou And Cai, 2004),
Amino Acids

Hausman, Autocorrelation Test and Heteroscedasticity,...
Hausman test
Hausman test which usually accepted method of selecting between random and fixed effects which
is running on regression equation. Hausman (1978) provided a tectonic change in interpretation
related to the specification of econometric models. The seminal insight that one could compare two
models which were both consistent under the null spawned a test which was both simple and
powerful. The so–called 'Hausman test' has been applied and extended theoretically in a variety of
econometric domains. We focus on the construction of the Hausman test in a variety of panel data
settings, and in particular, the recent adaptation of the Hausman test to semi–parametric and
nonparametric panel data models. A formal application of the Hausman test is given focusing on
testing between fixed and random effects within a panel data model. Mostly fixed effects are
accepted way to run with panel data as they always present consistent outcomes but may not be the
most effective way to implement. On the other hand, random effects usually provide to the
researcher better P–values as it considered to be a more active estimator, so researcher can study
random effects if it is reasonable to do so. Moreover, Hausman test choose a more effective model
compared to a less efficient as consistent model should presents robust estimates and consistent
results owing to the more efficient model.
Autocorrelation test
Another terms sometimes used for describe Autocorrelation these are "lagged

What Is MTM-SVD?
Where each row represents the measurements from different taper K at the same sensing node, and
each column represents the measurements from different sensing node at the same taper. Based on
these measurements he applied SVD, and he got the power estimation from singular value, as it is
represented the power at this pin. In this paper cite{alghamdi2009performance} the author
evaluated the performance of MTM–SVD for setting specified number of sensing nodes with the
chosen MTM parameters. The author cite{alghamdi2010local} continued the previous work, by
exploring the probability of detection, miss detection and false alarm, in order to evaluate the
MTM–SVD performance. On the other hand,some papers worked on reducing the time consuming
... Show more content on Helpwriting.net ...
Therefore, the measurements taking from Multitaper will be arranged in 3dimension matrix, where
the third dimension is the consecutive OFDM blocks and the others are CR antennas and DPSS
measurement. The measurements will be taken and will be applied to higher order tensor
decomposition , in order to take new singular value computation as the tensor core G(l,m,k)
.Consequently the decision will be taken as the sum of squared singular value ,then compare it by
threshold . Although MTM–SVD provides reliable detection performance, in the worst
environmental conditions and specific SNR the system suffers from some degradation performance.
2.3.3 subsubsection{ Weighting MTM:} The lower–order Eigen spectrum of the MTM method has
an excellent bias property. However,as the index k increases toward the time bandwidth product
NW, the method experiences some degradation in performance. Therefore
Thomsoncite{thomson1982spectrum} introduces a set of weights ${dk(I)}$ which it effects on
down–weighting the higher order spectrum . Haykin follows him in this paper
cite{haykin2007multitaper}, where he proposed a simpler solution for computing the adaptive
weight. Accordingly, he derived an adaptive weight by minimizing the mean square error between
an exact coefficient of incremental random process and coefficient of $ k^{th} $ samples.} 2.3.4
subsubsection{Compressive SVD–MTM Sensing :} As we

The Importance Of Sea Temperature Anomalies
The oceans play an important role in the climate system owing to the interannual and longer
timescale variability in sea surface temperature (SST). Hasselmann (1976) proposed that this
climate variability could be represented by a stochastic first order auto–regressive process
(AR1–process) and should be considered as the null hypothesis for extra–tropical sea surface
temperature anomalies (SSTA). According to this concept, SSTAs quickly responds to the
atmospheric heat fluxes at short time period and the heat capacity of the ocean integrates the
SSTA variability on a longer time period. Frankignoul and Hasselmann (1977) have further
suggested that the atmospheric forcing for SST anomalies follows a spectrum of white noise with
constant ... Show more content on Helpwriting.net ...
Thus the broad structure of the SSTA spectrum is determined by the depth of the ocean ML and
atmospheric process.
Attempts to include additional processes, such as Ekman transport, entrainment of sub–mixed layer
thermal anomalies (Dommenget and Latif 2002; Deser et al. 2003; Lee et al. 2008), state–dependent
noise (Sura et al. 2005) and the re–emergence associated with seasonal change in MLD (Schneider
and Cornuelle 2005) has shown to increase the SSTA variance at annual and longer timescales.
However, studies have demonstrated that the SST variability at some part of the oceans cannot be
represented by a simple AR1–process (Hall and Manabe
1997; Dommenget and Latif 2002, 2008). The inconsistency arises from the exchange of heat
energy in the mixed layer and sub–mixed layer in the ocean.
The strong seasonal cycle of the MLD can strengthen the persistence of SSTA from one winter to
the following. The timescale in which the subsurface temperature anomalies entrain to the surface
(nearly 1 year) is expected to influence the spectral variance of SSTA. Möller et al. (2008) have
shown a peak in the annual time period in the power spectrum of midlatitude
SSTA that is associated with the re–emergence. Figure 5.3 illustrates the power spectrum of
SSTA and 90% significance level (shaded), presented in different ways (taken from Moller et al.
(2008)). Figure 5.3a express the spectral variance density, while figure 5.3b

The Effect Of Effect On Emerging Stock Markets Of Four...
Part 3 – Data and Methodology
3.1 Data Description The purpose of this study is to investigate the presence of January effect in
emerging stock markets of four Southeast Asia countries: Malaysia, Thailand, Philippine and
Indonesia, for the period of January 2012 until December 2015, which is the most recent period after
the financial crisis in 2007–2008. The financial crisis would affect the behaviour of the stock
markets and thus the stock price might not reflect its true value. As the most recent economic crisis
is believed to have ended in Fall 2011 (Elliott 2011; Weisenthal 2013), this study will focus on the
most recent 4–year period, from January 2012 until December 2015. The four Southeast Asia
countries are selected because there are limited studies about them. Furthermore, they are the only
Southeast Asia countries being included in MSCI Emerging Markets Index as of 2016. Thus it is
worth examining the efficiency of the stock markets of these high growth emerging markets.
Daily equity market indices for four Southeast Asia countries will be collected from Yahoo Finance
and DataStream. The daily price index is collected instead of monthly price index because this study
attempts to examine if the January effect is stronger on the first five days of January. The indices are
FTSE Bursa Malaysia KLCI Index (KLCI) for Malaysia, SET Index for Thailand, Philippine Stock
Exchange Composite Index (PSEi) for Philippine and IDX Composite Index for Indonesia. Since
these

Relationship Between Vietnamese Stock Price Relative On...
METHODLOGY
The purpose of this paper is concentrated on relationship between Vietnamese stock price relative to
exchange rate and United State stock market. In order to have a better view about this relationships,
the suitable econometrics model will be used in the research are OLS and ARMA. To determine the
correlation, coefficients among the variables from the test we will be able to find out the β, R2, P–
value, Standard Error, Durbin–Watson stat statistic etc... With the time series dataset, in other to get
a good forecast, the regressions will be run and tested on EVIEW program. The main model will be
use is:
VNSP= β_0 + β_1S&P500 + β_2VNER + ε (e1)
By using OLS model we can determine how much the dependent variable is influenced by the
independent variables. The null and alternative hypothesizes will be as following:
VNSP Viet Nam's monthly stock price index β Beta
S&P500 American monthly stock market index
VNER Viet Nam's monthly exchange rate ε Error term
H_0: The Viet Nam's monthly stock price index is not influenced by American monthly stock
market index and Viet Nam's monthly exchange rate.
H_1: The Viet Nam's monthly stock price index is influenced by American monthly stock market
index and Viet Nam's monthly exchange rate.
MODELS
The program will be used to run regressions and analyze the outputs is EVIEW8. The Least Squares
method of estimation is used for the analysis of the data. The least squares method of estimation is
preferred

Compare And Contrasting Fama's Articles
Comparing and contrasting Fama's articles (1971, 1990), this work will critically assess the
development of EMH during the 1970 to 1991 period. Firstly, it provides the reader to a short
introduction to the EMH and to comparison the major changes between two articles. Thereafter, the
main focus will be concentrated on second article and its critical evaluation of results obtained. The
main purpose of the capital market is to provide the investors with accurate signals for resource
allocation. It is possible when market security prices do "fully reflect" available information
providing the opportunity to make production–investment decisions. Such markets are also called
the "efficient". A precondition for this is that information and trading costs equal to 0. Moreover, the
joint hypothesis problem is the main obstacle about market efficiency because it must be tested
jointly with some asset pricing models. In article of 1971, Fama categorised market efficiency into
three main forms. Weak form is based on the historical data of the stock prices. The semi–strong
form tests how efficiently prices adapt to the publically available information. The third form is
concerned whether any given market participants having monopolistic access to the creation of the
stock prices. Final Draft – Return Predictability: In short, the new work rejects the old constant
expected returns model that seems to perform well in the early work. It is rejected due to such
findings as

The Role Of Indian Fdi On Nepalese Economic Growth
3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of
Investment and Export in real terms from the period 1989/90 to 2013/14. The concerned variables
are transformed into logarithm and hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t
〖LnI〗_t and 〖LnX〗_t . Fully Modified Ordinary Least Squares (FMOLS) is the main
econometric methodology used in this paper to examine the role and impact of Indian FDI on
Nepalese economic growth. The FMOLS of economic growth of Nepal on Indian FDI augmented
with level of investment and export has been used to find the magnitude of long run relationship
between the variables under study. GDP is taken as the proxy for Nepalese economic growth. Some
attention is necessary while employing FMOLS test. The variables under study must be
cointegrated. So before applying the FMOLS we examine the cointegration by method of Johansen's
(1990) cointegration test. Prior to employing the Johansen's Cointegration test we perform unit root
test using ADF method. FMOLS method was designed by Phillips and Hansen (1990) to estimate
the cointegrating regressions. This method employs a semi–parametric correction to eliminate the
problems created by long run correlation between cointegrating equation and stochastic regressors
innovations. This method is used to modify the least squares to account for serial correlation effects
and for the endogeneity in the regressions that result from the existence of cointegrating

Unit 3 Autocorrelation Test Paper
a. R2 value generated by empirical estimation regression model individual very high but many
independent variables that are not significantly affect the dependent variable.
b. Analyzing the correlation matrix of the independent variables. If the correlation between
independent variable is fairly high (generally above 0.90), then this is an indication
multicollinearity. Multicollinearity can be appear due to the combined effect of two or more
independent variables.
c. Multicollinearity can also be seen from (1) the value of tolerance and (2) variance inflation factor
(VIF). Both these measurements indicate each variable which independent explained by other
independent variables. In a simple understanding of each independent variable becomes the
dependent variable (tied) and regressed against other independent variables. Tolerance measuring
the variability of ... Show more content on Helpwriting.net ...
This shows the size of each independent variable which explained by other independent variables.
Tolerance measures the variability of the variable independently chosen that are not explained by the
other independent variable. So a low tolerance value equal to the value of high VIF. Cutoff value
that is commonly used to indicate the presence multicollinearity is the value of tolerance 0.10 or
equal to the value of VIF 10 (Ghozali, 2005).
3.5.2.3 Autocorrelation Test
Autocorrelation test aims to determine whether there is a correlation between bully errors in period t
to period t–1 (previously). If correlation occurs, then there is a problem called autocorrelation.
Autocorrelation appears because successive observations over time are related to each other.
This problem arises because the residual (error bullies) are not independent of one observations to
other observations. It is often found in the time series data (time series) because of "disturbances" on
an individual / group tend affect the "disturbance" at the individual / group the same period next. A
good regression model is free of

Absolute Best Model For Forecasting
The objective of this experiment was to find the best possible model for forecasting. I will use a
series of tests both visual and statistical to find the absolute best model for forecasting the data set.
The forecast will be made for the conglomerate Wal–Mart. I start my test by taking the time series
plot graph of the data. This indicates whether the data has a seasonal or quarterly trend, and if there
is a time trend. I also run a trend analysis on the data set. I compare the graphs through trend
analysis, and choose the graph with the smallest amount of error. I have elected to use the quadratic
trend model because the mean square deviation (MSD) is much lower in terms of error compared to
the linear graph. My other objective, is to ... Show more content on Helpwriting.net ...
My first step is to remove the quarterly trend by taking a fourth difference. Taking a fourth
difference will remove the quarterly trend that could be affecting my ability to determine if there is a
time trend. I used 16 numbers of lags because we are using quarterly information instead of
seasonal. As you can tell by the above graph there is no longer any seasonal data. However, there are
4 or more blue measure points above the red line which indicates there is a time trend. The red line
symbolizes Bartlett's test, which states that a consecutive string of 4 or more spikes above the red
line indicates a time trend. I will now take a first difference of the fourth difference of revenue. This
is basically taking out both the quarterly and time trend that could be potentially distorting the data.
Now on the graph on the right, is the result of taking the first difference of the fourth difference of
revenue. It is now very clear that the time trend is no longer existing within the data set, and the
quarterly trend has also been removed. It is somewhat concerning that there are spikes at various
points. More specifically at the first and fourth lag which I will consider when adding the partial
autocorrelation tool. The partial autocorrelation and autocorrelation functions, are used to determine
if there is an autoregressive, moving average, or mixed model as I mentioned earlier. The AR
represents trend and quarterly values, while MA tends to represent the

Time Series Analysis
V.I.1.a Basic Definitions and Theorems about ARIMA models
First we define some important concepts. A stochastic process (c.q. probabilistic process) is defined
by a T–dimensional distribution function.
Time Series Analysis – ARIMA models – Basic Definitions and Theorems about ARIMA models
marginal distribution function of a time series
(V.I.1–1)
Before analyzing the structure of a time series model one must make sure that the time series are
stationary with respect to the variance and with respect to the mean. First, we will assume statistical
stationarity of all time series (later on, this restriction will be relaxed).
Statistical stationarity of a time series implies that the marginal probability distribution is time–
independent ... Show more content on Helpwriting.net ...
A practical numerical estimation algorithm for the PACF is given by Durbin
(V.I.1–29)
with
(V.I.1–30)
The standard error of a partial autocorrelation coefficient for k > p (where p is the order of the
autoregressive data generating process; see later) is given by
(V.I.1–31)
Finally, we define the following polynomial lag–processes
(V.I.1–32)
where B is the backshift operator (c.q. BiYt = Yt–i) and where

(V.I.1–33)
These polynomial expressions are used to define linear filters. By definition a linear filter
(V.I.1–34)
generates a stochastic process
(V.I.1–35)
where at is a white noise variable.
(V.I.1–36)
for which the following is obvious
(V.I.1–37)
We call eq. (V.I.1–36) the random–walk model: a model that describes time

Energy Detection Based Spectrum Sensing Method
In energy detector, the received signal is first filtered with a band pass filter in bandwidth to
normalize the noise variance and to limit the noise power. The output signal is then squared and
integrated as follows: for each in–phase or quadrature component, a number of samples over a time
interval are squared and summed. The conventional energy detection method assumes that the
primary user signal is either absent or present and the performance degrades when the primary user
is absent and then suddenly appears during the sensing time. An adaptive method to improve the
performance of Energy detection based spectrum sensing method is proposed .In this proposal, a
side detector is used which continuously monitor the spectrum so as to improve the probability of
detection. The Primary user uses a QPSK signal with a 200 kHz band–pass bandwidth (BW). The
sampling frequency is 8 times the signal BW. A 1024–point FFT is used to calculate the received
signal energy. Simulation results showed that when primary users appear during the sensing time,
the conventional energy detector has lower probability of detection as compared to the proposed
detector. The performance of energy detector is characterized by Receiver Operating curves usually.
AOC (Area under the Receiver Operating curves) is used to analyze the performance of the energy
detector method over Nakagami fading channels. Results showed that a higher value of fading
parameter leads to larger average AUC, and

Event Study of Saic Stock Price
Newcastle University Business School
MA International Financial Analysis 2010/11
NBS8002
Techniques For Data Analysis –––––––––––––––––––––––––––––––––––––––––––––––––
SAIC Stock Prices and Its Participation in GM's IPO (Keywords: Event Study, Daily Stock Return,
the OLS Market Model, SAIC, IPO)
Tutors Name: A.D Miller Student Name: Chen Kai (Jimmy) Student Number: b109000774 Date of
submission: 10th /May/2011 Words Count: 5000
Table of Contents * Introduction * Overview of Market Efficiency and Event studies 1. Market
Efficiency ... Show more content on Helpwriting.net ...
The five events are correlated and occurring over approximate five months from 18th August 2010
to 13th December 2010.
Choice and Collection of Data
In order to study how stock prices react to these events, approximate three years of continuous daily
stock price are chose, beginning at 17th March 2008 and ending more than three months after the
final event at 22nd April 2011. In addition, SHANGHAI Stock Exchange Index (SSE) is adopted as
a proxy of the market portfolio.
The three–year SAIC stock price data and its corresponding SSE index are obtained from
finance.yahoo.com, as it provides dividend–adjusted closing prices. The two data are ordered in time
in Excel (Sort Ascending). It is found that 46 SAIC daily stock prices are missing due to suspension
of trading, therefore; 46 corresponding SSE daily index are removed in order to match up dates on
the two data series.
Estimation Period and Test Period
Given the event date and stock price data, the EP and TP can be constructed in order to estimate the
normal returns and abnormal returns respectively.
The model parameters are estimated from the EP and therefore the AR can be calculated within the
TP (Strong, 1992). Explicitly, the AR which

Marginal Cost and Correct Answer Essay
Question 1 The primary objective of a for–profit firm is to ___________. Selected Answer: Correct
Answer: 5 out of 5 points maximize shareholder value maximize shareholder value Question 2 5 out
of 5 points The flat–screen plasma TVs are selling extremely well. The originators of this
technology are earning higher profits. What theory of profit best reflects the performance of the
plasma screen makers? Selected Answer: Correct Answer: innovation theory of profit innovation
theory of profit Question 3 5 out of 5 points The Saturn Corporation (once a division of GM) was
permanently closed in 2009. What went wrong with Saturn? Selected Answer: Correct Answer:
Saturn sold cars below the prices of Honda or ... Show more content on Helpwriting.net ...
Selected Answer: Correct Answer: autocorrelation autocorrelation Question 17 5 out of 5 points
Consumer expenditure plans is an example of a forecasting method. Which of the general categories
best described this example? Selected Answer: Correct Answer: survey techniques and opinion
polling survey techniques and opinion polling Question 18 5 out of 5 points For studying demand
relationships for a proposed new product that no one has ever used before, what would be the best
method to use? Selected Answer: Correct Answer: consumer surveys, where potential customers
hear about the product and are asked their opinions consumer surveys, where potential customers
hear about the product and are asked their opinions Question 19 If two alternative economic models
are offered, other things equal, we would Selected Answer: 5 out of 5 points select the model that
gave the most accurate forecasts select the model that gave the most accurate forecasts Correct
Answer: Question 20 5 out of 5 points The use of quarterly data to develop the forecasting model
Yt = a +bYt−1 is an example of which forecasting technique? Selected Answer: Correct Answer:
Time–series forecasting Time–series forecasting Question 21 If the

Essay On Cd Metal
Interpolating Cd Metal in Soil Using Spatial Techniques in Metropolis Areas of Faisalabad Abstract
Rapid industrialization and urbanization in recent decades has resulted in large emissions of heavy
metals especially in urban soils around the world. Soil contamination with heavy metals may pose
serious threat to environmental quality and human health due to their toxicity even at low
concentration. Cadmium (Cd) is one of the toxic heavy metals that has high mobility in soil–plant
system and can accumulate in plant and human bodies. In this study, we determined the content of
Cd in urban and peri–urban soils of four towns (Lyallpur, Iqbal, Jinnah and Madina) of Faisalabad.
The samples of surface soil (0–15 cm) were collected from ... Show more content on
Helpwriting.net ...
Due to massive increase in population and so residential colonies in Pakistan many industrial units
once located outside of big cities has now surrounded by living places. This is particularly true for
Faisalabad Metropolitan, where many industrial units once outside city have been surrounded by
many residential colonies. Most of these industrial units release untreated wastewater and gaseous
pollutants in soil–water and air compartments of atmosphere. The waste water released from
industrial units is being used by farmers for growing several vegetables and fodder crops. The
continuous use of such waste water for irrigation is introducing many heavy metals in soils. These
heavy metals especially cadmium (Cd) from soils can easily enter food through the consumption of
food crops grown on metal contaminated soils. Owing to high mobility in soil–water–plant nexus,
Cd is easily entered in food chain and thus can pose serious threat to biological molecules and
affects several body functions in human body. (Momodu and Anyakora, 2010). Soil is a
heterogeneous body that shows large variations in most of the properties (physical, chemical and
biological). Although many factors and processes of soil formation contribute to the variation in soil
properties, time and space are the two most important

The Importance Of Drinking Water In Bangladesh
Introduction
Safe drinking–water is essential for healthy life, and United Nations (UN) General Assembly
declared safe and clean drinking–water as a human right essential to the full enjoyment of life [1].
Moreover, the importance of water, sanitation and hygiene for health and development has been
reflected in the outcomes of a series of international policy forums [1]. These have also included
health and water–oriented conferences, but most importantly in the Millennium Development Goals
(MDG) adopted by the General Assembly of the UN in 2000. The UN General Assembly declared
the period from 2005 to 2015 as the International Decade for Action, "Water for Life" [1]. Access to
safe drinking–water is important as a health and development issue at national, regional and local
levels. Bangladesh, a developing country from South Asian (SA) region also takes several steps for
ensuring sanitation and safe drinking water facilities among the people. As a result, Bangladesh has
made great progress in this sector. The government also claimed that it has achieved the MDG
indicator of ensuring safe drinking water for 85% people of the country. According to different
demographic and health surveys, the percentage of using improved sources of drinking water is
about 98% (reported in the latest two surveys Multiple Indicator Cluster Surveys (MICS) 2012–13
and Bangladesh Demographic and Health Survey (BDHS) 2014) [2,3]. But, this achievement
statistics are overlooking the shortcomings.

A Study Of The Economic Forecasting Of New One Family...
A STUDY OF THE ECONOMIC FORECASTING OF NEW ONE FAMILY HOUSEHOLDS
SOLD IN THE US – AN ANALYSIS Context and Objective of the Analysis The US housing
industry has witnessed a downward trend post 2005 due to deteriorating macroeconomic conditions
in the United States. The steep decline in the last 5 years has led to investigations on the future of
the industry and understands the way forward for the industry. The report answers the following
questions: How long is the fall in the industry going to continue? When is the recovery expected in
the Housing Market? What is the future of the industry? The report is an attempt to understand the
trends in the US New One Family Household market (herein referred to as NHS) and forecast the
NHS ... Show more content on Helpwriting.net ...
Detailed study of the forecasts reveals that the housing industry is in a consolidation phase and the
recovery of the industry is not expected in the next one year (2011). Historical Trend of NHS and
impact of external factors – A qualitative analysis The US National Housing market, specifically the
One Member Housing Market has seen a steep decline since the latter half of the last decade. The
NHS data for the last 35 years (1975–2010) has been shows in the figure below. From the data, three
specific trend profiles of the NHS can be witnessed; the period from 1975 to 1991, where the NHS
showed a stable trend, the period from 1991 to 2005 where the NHS showed a steady acceleration
and the period from 2005 onward showing a steep decline in the NHS numbers. Figure 1. NHS data
(1000s) 1975–2010 A high level visual analysis of the data reveals a significant seasonality and
trend factor. The next section we will attempt to understand the quantitative impact of the trend and
seasonality factors. Relationship between Housing Data and Mortgage Rate & Disposable Income
The decline can be attributed to the decline in the macroeconomic conditions in the US. However,
an in–depth analysis of impact of specific economic indicators would be essential understand the
way forward for the NHS. The data provided

The Relationship Between Economic Growth And Its...
The relationship between economic growth and its determinants has been examined extensively.
One important issue is whether population leads to employment changes or employment leads to
population changes (do 'jobs follow people' or 'people follow jobs'?) To explain this interdependence
between household residential choices and firm location choices, a simultaneous equations model
was initially developed by Carlino and Mills (1987). This modeling framework has also been
applied in various studies to investigate the interdependence between migration and employment
growth or migration, employment growth, and income jointly determined by regional variables such
as natural amenities (Clark and Murphy, 1996; Deller, 2001; Waltert et al., 2011), public land policy
(Duffy–Deno, 1997, 1998; Eichman et al., 2010; Lewis et al., 2002, Lewis et al., 2003; Lundgren,
2009), and land development (Carruthers and Mulligan, 2007). In the Carlino–Mills (1987) model,
the assumption is that households and firms are spatially mobile. Also, it is assumed that households
migrate to maximize their utility from the consumption of private goods and services and use of
non–market goods (amenities) and firms locate to maximize their profit whose production costs and
revenues depend on business conditions, local public services, markets, and supply of inputs. In
addition, these assumptions indicate that interdependence between employment and household
income exists because household migrate if they

Correlation Between Land Use Land Cover And Water Quality...
Why use correlation and regression in your work
It is very important in selecting the right kind of statistical, presentation, and analytical methods to
determine the relationships between land–use land–cover and water quality parameters. This is very
important because of the issues associated with spatial autocorrelation and non–independence of
sampling site that often accompany research into water quality and land use.
Literally, autocorrelation is where a variable is said to be correlated with itself and it states that pairs
of subjects that are close to each other are more likely to have values that are more similar, and pairs
of subjects far apart from each other are more likely to have values that are less similar. The spatial
structure of the data in autocorrelation refers to any patterns that may exist in this nearness or distant
relationships. When data are spatially autocorrelated, it is possible to predict the value at one
location based on the value sampled from a nearby location using interpolation methods. When
autocorrelation does not exist, then the data is said to be independent.
Because of the qualitative nature of water quality analysis, there are a lot variety of statistical
analyses that can be performed on water quality data for reporting and interpretation purposes.
Though most of this statistics can be said to be intense, many inferences can be made from simple
descriptive statistics such as mean, minimum, maximum, median, range, and standard

Analysis Of Predictability And Efficiency Of Pt Astra Agro...
Part A: Analysis of Predictability and Efficiency of PT Astra Agro Lestari Tbk and PT Kalbe Farma
Tbk Market efficiency, predictability and its importance for stock traders and/or other market
participant There is a saying that no one can beat the market systematically when market is efficient
because no one can predict the return. Market is said to be efficient when all available information
fully and quickly reflected in the security price. Efficiency can be achieved when market is perfectly
competitive where there is no transaction cost (or lower than expected profit), no transactional delay
and all traders behave rationally. Perfect competitive market made arbitrage trading (buy in one
market and sell in another market) possible ... Show more content on Helpwriting.net ...
Investors cannot predict future value using past value (or past error) because price changes from one
period to the next period, hence technical analysis will be useless. Security's prices in semi–strong
form fully reflected all publicly available information, including its all past value. Investors cannot
obtain abnormal return by using fundamental analysis. While strong form efficiency is achieved
when security prices fully reflect public and privately held information, including past value. As a
consequence, information can be obtained by every participant and no one can achieve systematic
abnormal return. Market can be weak–form but not semi–strong or strong but strong form efficient
market must be weak–form and semi–strong. Investment strategy in efficient and not efficient
market If market is efficient, investors should adopt passive investment strategy (buy and hold)
rather than active strategy because active strategy will underperform due to transaction cost.
Investor will buy asset that they think the intrinsic value is lower than market value, and vice versa.
If the market is not efficient, investors will buy securities that replicate market index portfolio,
which is in the efficient frontier line and have low transaction cost. Technical way of expressing
market efficiency E [(Rt+1 – Rf) ǁ Ωt ] = 0, where Rt = rate of return; Rf = return on risk–free
assets; Ωt = relevant information available at t. Market is efficient if

Computational Model of Neural Networks on Layer IV or...
Topic: Computational Modeling of Neural Networks on Layer IV of Primary Visual Cortex
Confirms Retinal Origin of Orientation Map
Results section Orientation selectivity is one of the properties of neuron in primary visual cortex that
a neuron response maximally when particular orientation of stimulus is given. The orientation map
is a map showing the orientation preferences of cortical neurons in primary visual cortex. This
research provides evidences for support of the theory posit that the orientation selectivity map is a
product of a Moiré interference pattern that originates in retinal ganglion cells. This paper shows
that interactions between excitatory neurons and inhibitory neurons in neuron network modeled by
NEURON simulator having a Moiré interference pattern which results in an orientation selectivity
map on the primary visual cortex.
The LGN neural network
The Feed Forward Input Network
The On and Off mosaics of magnocellular LGN cells were created. Examples of the mosaics are
shown in the figure 5. The networks act as feed forward input to the cortical neural network. Figure
5. The On and Off KGN mosaics. A) The ideal mosaic when there is no spatial noise.
B) The mosaics that created following the real physiological data constraints.
A shows more interference pattern than B.
Layer 4C of Primary Visual Cortex Cortical Network Model
There are two types of cortical neurons being considered in the model, excitatory neurons and
inhibitory neurons.

Obesity And The United States
Compared to other countries, the United States was reported to have the second highest rate of
obesity in the world after Mexico. Over the past decade, cases of obesity have triplicated in the U.S.,
affecting more than one–third (34.9% or 78.6 million) of the adults (Ogden et al. 2014). Given the
current trends, it is projected that 42% of the U.S. population will be obese by 2030 (Finkelstein et
al. 2012). Aside from its nefarious impact on the overall quality of life of the affected individual on
a micro level, obesity has an enormous economist cost on the US healthcare system. In their
extensive annual medical spending report, Finkelstein et al. (2012) indicated that the annual medical
cost for obesity in the US amount to $147 billion ... Show more content on Helpwriting.net ...
According to the most recent data, two states have adult obesity rates above 35 percent, 20 states
have rates at or above 30 percent, 43 states have rates at or above 25 percent and every state is
above 20 percent. (State of Obesity 2013). Studies (Arcaya et al. 2013; Burdette and Whitaker 2004)
have identified various factors that play a role in the state of this current conjuncture. Findings on
the subject are not uniformed however. Papas et al. (2007) have identified twenty studies in their
systematic literature review that investigate the effect of environment's structure on the rate of
obesity. While 17 of those studies show a significant relationship between those two variables, three
of them found no relationship. At a county–level, only two studies (Holzer, Canavan and Bradley
2014; Slack, Myers, Martin et al. 2014) have investigated the geographical variability in the rate of
obesity. They discovered that higher obesity rates were linked with counties with lower number of
dentists per capita, higher percentages of African Americans, higher rates of unemployment, lower
rates of educational attainment and fewer adults who engaged in regular physical activity. The
results of these two studies provided up to date evidence on a national scale. In the end, the situation
remains, the same: the dynamic between local level factors associated with this public health

Linear Accounting Valuation When Abnormal Earnings Are Ar...
Referee Report on: Jeffrey L. Callen and Mindy Morel (2001), Linear Accounting Valuation When
Abnormal Earnings Are AR (2), "Review of Quantitative Finance and Accounting", vol. 16 pp 191–
203 Introduction In this study, Callen and Morel (Callen & Morel, 2001) compare the linear
information dynamics of Ohlson model (Ohlson, 1995) with AR (1) process, which is used in
Ohlson's research, and AR (2) process for earnings, book values and dividends. The purpose of this
research is to evaluate the forecasting ability of the Ohlson model with AR (2) process. The authors
reference the methods in Myers' research (Myers, 1999). And, they find that there is no significant
difference between the results of original model and the new model, though the ... Show more
content on Helpwriting.net ...
The valuation equation with AR (1) process is following: V_t^1=y_t+(R_f ω_0)/((R_f–ω_1 )(R_f–
1))+ω_1/((R_f–ω_1)) x_tâ The AR (2) dynamic (Callen & Morel, 2001) can be expressed as:
x_(t+1)â=ω_0+ω_1 x_tâ+ω_2 x_(t–1)â+ε_(t+1) So, the valuation equation (Callen & Morel,
2001) is: V_t^2=y_t+(R_f^2 ω_0)/((R_f^2–ω_1 R_f–ω_2 )(R_f–1))+(ω_2 R_f)/((R_f^2–ω_1 R_f–
ω_2)) x_(t–1)â+(〖R_f ω〗_1+ω_2)/((R_f^2–ω_1 R_f–ω_2)) x_tâ Besides, the sample is
selected from 676 firms with at least 27 years, a total of 19,789 firm–years statistics. These data is
selected by three standards, including long–term data (at least 27 years), positive book values and
non–financial firms. By panel data techniques, the writers find that the AR (2) dynamic is poorer in
explaining V_t when comparing with AR (1) dynamic. Meanwhile, the results indicate that both A
(1) and AR (2) dynamics underestimate equity values, though the latter has a slight advantage.
Major Concerns The researchers select long–term statistics (up to 34 years) to test the dynamic
model. It is more accurate by using long–term data since some shocks in short run may impact the
results. The writers not only provide the result that AR (2) dynamic does not have obvious
improvement when comparing with AR (1) dynamic, but also state their explanations, which offers
various directions of following researches. Minor Concerns This study might be stricter if the
researcher added stationary test. Since most variables are

Summary: Forecasting Profitability and Earnings
Summary of Forecasting Profitability and Earnings
In the competitive environment, there is a strong prediction in economic theory that profitability is
mean reversion both within and across industries. For instance, under competition, firms will leave
relatively profitless industries and turn into relatively high profitable industries. Some companies
introduce new products and technologies that bring more profitability for an entrepreneur.
Otherwise, the expectation of failure which makes companies with low profitability motivations to
distribute capital to more productive uses.
Mean reversion represents that changes in earnings and profitability is predictable to a certain
extent. However, predictable variation in profitability and ... Show more content on Helpwriting.net
...
Lagged changes in the profitability are equal to Yt/At minus Yt21/At21. DFEt is equal to Yt/At
minus E(Yt/At). Table one indicates that when there are some lagged changes in profitability, CPt, is
exclusive used to explain CPt11, and slope of CPt is forcefully negative; commonly, the changes in
profitability of the companies from t to t+1 back up 30 percent of lagged change. But for average
reversion which leads slope of CPt close to 0. Therefore, there is little as well as statistically
dependable negative autocorrelation in the change in profitability. Our evaluation of average
reversion rate of the profitability is 38 percent per year.
In conclusion, differences in risk bring differences in the expected profitability of a firm.
Furthermore, Yt/At is the noisy agent for the true economic profitability. Finally, differences in the
expected profitability of a firm can be the results of the monopoly rents. If we suppose all
companies revert toward an overall balance level of profitability, then:
[pic]
Section II is the model to use for nonlinear average reversion. We expand the model as:
[pic]
Table one shows that there is nonlinearity in autocorrelation of changes in expected profitability. It
is similar to that studied by Brooks and Buck–master (1976) as well as Elgers and Lo (1994) about
the changes in

The Correlation Between The Value Of Time Series Of...
Autocorrelation
Autocorrelation is defined as the correlation between the value of time series at a specific time and
previous values of the same series (Reference). In other words, with time series what happens in
time t contains information about what will happen at time t+1. Autocorrelation plots are a
commonly–used tool for checking randomness in a data set. This randomness is ascertained by
computing autocorrelations for data values at varying time lags. If random, such autocorrelations
should be near zero for any and all time–lag separations. If non–random, then one or more of the
autocorrelations will be significantly non–zero. The autocorrelation plots can provide answers to
questions such as are the data random? Is an observation related to an adjacent observation? Is the
observed time series white noise, sinusoidal or autoregressive? They help in understanding the
underlying relationship between the data points. The autocorrelation plots of 4 time series of heating
operating system are as follows :
a. Supply temperature setpoint :– The plot starts with a high correlation at lag 1 which is slightly
less than 1 and slowly declines. It continues to decrease until it becomes negative and starts showing
an increasing negative correlation. The decreasing autocorrelation is generally linear with little
noise. Such a pattern in the autocorrelation plot is a signature of "strong autocorrelation", which in
turn provides high predictability if modeled properly. b. System

Temporal Variation Of Municipal Water Quality

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