Iterative Multivariate Regression For Correlated Responses

Iterative Multivariate Regression For Correlated Responses
Iterative Multivariate Regression for Correlated Responses
Multivariate regression is a standard statistical tool that regresses independent variables (predictors)
against a single dependent variable (response variable).The objective is to find a linear model that
best predicts the dependent variable from the independent variables. In order to explain the data in
the simplest way, redundant or unnecessary predictors should be removed. Such eliminating process
is needed for the following reasons. First, unnecessary predictors will add noise to the estimation of
other quantities that we are interested, causing loss in degrees of freedom in statistical point of view.
Second, if the model is to be used for prediction, we can save time and/or money by not measuring
redundant predictors. Finally, multi co–linearity is caused by having too many variables trying to do
the same job.
The residuals from multivariate regression models are assumed to be multivariate normal. This is
analogous to the assumption of normally distributed errors in univariate linear regression (i.e. ols
regression).
Multivariate regression analysis is not recommended for small samples.
The outcome variables should be at least moderately correlated for the multivariate regression
analysis to make sense.
Partial least squares (PLS) regression is a recent technique that combines features from and
generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict
a set of
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2121 unit information
ETF2121/ETF5912 Data Analysis in Business
Unit Information – Semester 1 2014
Coordinator and Lecturer – Weeks 7–12: Associate Professor Ann Maharaj
Office: H5.86
Phone: (990)32236
Email: ann.maharaj@monash.edu
Lecturer – Weeks 1–6: Mr Bruce Stephens
Office: H5.64
Phone: (990)32062
Email: bruce.stephens@monash.edu
Unit material: No prescribed textbook
Unit Book: available on the Moodle site.
Exercises:
available on the Moodle site.
Software:
EXCEL.
Recommended Reference Books
Berenson M.L., Levine D.M., Krehbiel T.C., Watson J., Jayne N., Turner, L.W.,
O'Brien M., Basic Business Statistics, Concepts and Applications, 2nd Edition (2010) or 3rd Edition
(2013), Pearson, Australia.
Black K., Asafu–Adjaye J., Khan N., Perera ... Show more content on Helpwriting.net ...
ETF5912 students note that, if Assignment 3 is submitted after the due date, it will not be marked,
hence, resulting in zero marks for this assessment task.
Examination
There is a hurdle requirement in this unit. The learning outcomes in this unit require students to
demonstrate in the final examination comprehensive understanding of the topics covered in the unit.
This is demonstrated by the requirement that the student must attain a mark of at least 45% in the
final examination.
A student 's final mark is normally the sum of the marks obtained in all of the assessment tasks in
the unit. However, when a student fails the unit solely because of failure to satisfy the hurdle
requirement, a mark of 48 will be returned for the unit.
Refer to the faculty website for details:
http://www.buseco.monash.edu.au/esg/agu/policies/methods–of–assess.html Your final grade will be
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48 N
P
C
D
HD
if you score at least 50% overall, but less than 45% on the exam. if you score at least 50% overall,
and at least 45% on the exam. if you score at least 60% overall, and at least 55% on the exam. if you
score at least 70% overall, and at least 65% on the exam. if you score at least 80% overall, and at
least 75% on the exam.
3
ETF2121/ETF5912 Data Analysis in Business
Second marking
Where an assessment task is given a fail grade, that piece of work will

The Importance Of Mediating Effect On A Second Variable...
Mediating effect refers when a third variable intervenes between the related constructs and explains
why a relationship between them exists (Hair et al., 2009). There are several types of tests that can
test the mediating effects. McKinnon, Lockwood, Hoffman, West, & Sheets' (2002) summary of the
tests of significance of intervening variable effects is shown in Table 1.
In addition to the methods displayed in the summary table, recently other methods such as
Bootstrapping (e.g., Bollen & Stine, 1990) and the Monte Carlo Method (e.g., MacKinnon,
Lockwood, & Williams, 2004) have become popular. Bootstrapping is a non–parametric method
based on resampling with replacement that is performed many times (e.g., 1000 times). From each
sample, the indirect effects are computed and their distribution is generated. The Monte Carlo
Method is a computer simulation test of the indirect effect that is proposed by MacKinnon et al.
(2004).
Baron and Kenny (1986) provided a seminal work in their assertion of specific steps for testing
cross–section mediation models. They provided four conditions or steps for testing in their causal
steps approach, which linearly lays out specific relationships amongst the independent, dependent,
and mediator variable in four separate regression models. These four steps are noted below:
1) Demonstrate that the independent variable (X) is significantly related to the dependent variable
(Y). This is denoted as the c path.
2) Demonstrate that the independent

Real Estate Data Set
This task involves trying to give an educated guess on a linear regression model for pricing real
estate using a real facts date set using numbers and facts. Normally two approaches are used for
valuing a real estate property: income and sales comparison. The sales assessment approach values a
real estate property based on sale prices of similar properties. In this case the properties with
familiar individuality are basically on the same price level, it would be typical to use a linear
regression model to complete this demonstration. Our team will examine the significance of
numerous independent variables to a real estate value. Two important strategies to regression testing
is to run all test, and run a subset of the test based on a ... Show more content on Helpwriting.net ...
The correlation is 41% between Bdrm and Size. We have concluded that there is no problem of
multicollinearity in the data. We also found that Price and Size have relatively large scales compared
with other variables. So it is natural to consider the logarithmic form of these two variables. We will
run a new regression in which the regressor is log (price) and the regressand Size is replaced by its
logarithm log (size). The R–squared of this new model is around 60%, even a little lower than the
original model. The statistical significance of other independent variables keeps almost unaffected.
The analysis of coefficients in this model with logarithms is not intuitive. So we still stick to the
reduced linear model estimated in (a). We analyzed the OLS residuals to check for possible
heteroskedasticity for a cross–sectional data set. We computed the fitted values of Price and OLS
residuals in our model. Then residuals are sorted by fitted values of Price, and a scatter diagram of
residuals is plotted against the fitted values in Figure 2. It is observed that all points are fairly
scattered and there is no obvious pattern for the dependence of the residuals on the fitted values. We
conclude that a linear model with homoskedastic errors is satisfactory for this data set. We evaluated
the validity of this linear regression model by checking the accuracy of its prediction. The linear
model is estimated by using 100 out of 105

Regression of Baseball Player Salaries Essay
Introduction The Major League Baseball (MLB) organization is a group of baseball teams that have
made it to the Major League. The Major League Baseball data set provides the 2005 salaries of
multiple Major League Baseball (MLB) teams as well as individual salaries of players within 30
teams (Lind, Marchal & Wathen, 2008). The MLB data set gives information such as batting
averages, wins, salaries, home runs, errors, etc (Lind, Marchal & Wathen, 2008). Two specific teams
stand out of the information when looking at their stats; St. Louis and Kansas City. These two teams
are drastically different; one has the most wins out of the MLB data set, and the other has the least
wins. With St. Louis and Kansas City both being in the major league, ... Show more content on
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We could also test the differences in the season error rate of the two teams chosen and the stolen
base total based on the performance. When performing the regression hypothesis test on the data we
hope to prove that the there is a correlation between the performance of players and the salary that
they are paid. When looking at the base data we are to conclude that there is a difference but by
performing the hypothesis testing we can conclude that there are only some aspects of the sport of
baseball that actually combine performance and pay.
Regression Hypothesis Test
Regression Analysis r² 0.454 n 4 r 0.674 k 1 Std. Error 64.837 Dep. Var. Y ANOVA table
Source SS df MS F p–value
Regression 6,998.1407 1 6,998.1407 1.66 .3260
Residual 8,407.7423 2 4,203.8711
Total 15,405.8829 3
Regression output confidence interval variables coefficients std. error t (df=2) p–value 95% lower
95% upper
Intercept –95.3536 162.1630 –0.588 .6161 –793.0845 602.3773
X1 2.4551 1.9029 1.290 .3260 –5.7322 10.6425
Observation Y Predicted Residual
1 208.31 137.88 70.42
2 101.31 108.42 –7.12
3 36.88 42.13 –5.25
4 92.11 150.16 –58.05
In this regression analysis the x value is the wins, independent variable and the y value is the salary,
the dependent variable. The advantage to this

The Dataset Diabetes Details From Efron Et Al
Individual Project 1
Introduction
This report will cover the dataset diabetes details from Efron et al.(2003). Throughout this report,
we will explore the potential relationship between the ten predictor variables: age, sex, BMI,
average blood pressure, and six blood serum measurements (tc, ldl, hdl, tch, ltg, glu) on a
quantitative measure of disease progression one year after the baseline. There are 442 diabetes
patients, or records in the dataset. In this report, we will explore different methods and cross
validation techniques to understand these sample datasets leveraging linear regression, ridge
regression, lasso method and metrics like our MSE, the mean prediction error and standard errors of
the test dataset. First, we will ... Show more content on Helpwriting.net ...
With the positive coefficients, we will see an increase in one unit of each variable separately
compared with the advancement in diabetes. With a 0.05 parameter, the linear regression model
selects 5 predictor variables with significance, age, tc, ldl, tch, and glu. To validate the assumption,
we can plot the residuals versus the fitted values to see if there are any indications of signs of
random distributions. For the residual plot, we see there are no indications or violations of random
distribution and can calculate the MSE of the model, which is 3111.265. Next, we will leverage the
best subset method to select the predictor variables that are truly impactful to the model.
Best Subset Model
With our best subset method, we can leverage our lowest BIC metric to select the best model. We
can plot out best subset method and pinpoint the number of variables to select. The plot below
showcases that the lowest point or value of BIC, contains 6 variables. We leverage the BIC metric
because it places a penalty on models with more or many variables. Meaning, the mode variable a
model has, the bigger the penalty. We can review the coefficients, standard errors, t–value, and the
p–values for the best subset method with the significant six variables. Our MSE metric for the 6–
variable best subset model is 3090.483, a slight decrease from our linear regression model.
Best Subset Model with 10–fold Cross

The Report On A Fictional Blog Post
1. Summary
This report is analysing a fictional blog post.
Evidence to support anthropogenic climate change will be discussed.
Defence mechanisms that the author possibly uses will be evaluated.
This report will explain why the author of the blog is making the claims by explaining the ideas
surrounding conspiracy theories.
2. Introduction
The author of the blog post begins by describing two items in the news which they describe as
'worrying'. Firstly, the announcement of treble subsidies for so–called 'green' energy, which the
author explicitly links to the second point that a university has 'confirmed' global warming is
happening, due to carbon emissions. The author expresses how this is 'suspicious' as evidence to
support ... Show more content on Helpwriting.net ...
Evidence to support
The Intergovernmental Panel on Climate Change (IPCC) uses scientist from across the globe to
provide detailed information on the dangers that human activity has on the world's climate. The
planet is getting warmer due to the growth of emissions, such as methane and carbon dioxide
(Adams, 2015). To support this the graph below illustrates how surface temperatures have risen
dramatically in recent years (Kerr, 2006).
3. Defence mechanisms
Psychologists have sought to understand why individuals seem to hide away from information about
environmental issues. People generally become anxious when they encounter different emotions,
change or are challenged with undesirable ideas, to combat this defence mechanisms are triggered
(Adams, 2015). It is believed that there are nine different types of defence mechanisms, which are:
Denial: claiming that environmental problems do not exist (Adams, 2015), or that there is no real
threat.
Repression: disregarding any knowledge about environmental harm caused by humans.
Regression: having firm or overstated beliefs in leaders or experts to resolve environmental
problems as there is nothing the individual can do. Additionally, anger towards the risk of privileges
be revoked due to environmental issues.
Displacement: to avoid anxiety 'small steps' are taken, instead of recognising the actual changes that
are required.

Factors That Can Be Applied For Predicting Asset Prices Essay
3. Literature Review Generally, researchers are interested in studying factors that can be applied to
predict asset prices. Relied on Markowitz (1952)'s earlier work on diversification and modern
portfolio model, it is known that idiosyncratic risk can be diversified away and only systematic risk
matters to investors. Treynor (1961), Sharpe (1964) and Lintner (1965) independently contributed to
the CAPM to explain the relationship between the systematic risk factor and securities returns. This
emphasize the importance of systematic risk for investors to estimate asset price in order to earn
excess return on market. Based on the studies mentioned above, there is increasing popularity on
researches about the economic factors which are closely related to market. And financial institutions
conduct researches on investment strategies which are related to macroeconomic environment.
Sector rotation strategy is one of them. It is first introduced from National Bureau of Economic
Research (NBER) data on economic cycles sourcing back to 1854. This strategy invests in certain
sectors under different market conditions to consistently outperform the market. Sassetti and Tani
(2006) suggested the strategy could apply for dynamic asset allocation. Conover et al (2008) tested
the strategy and claimed sector rotation did earn consistent excess return on market. In details, it
specified that cyclical–sector stocks perform better during the market growths and defensive–sector
stocks

Relationship Between Expectations And Price Movements Essay
Motivation My motivation to look closer into the relationship between expectations and price
movements comes from my trading experience within the forex market. For the past two years, I
have been trading live within the forex market, with two years prior to this paper trading. What
attracted me to the forex market rather than the many other markets available to trade independently
was the high volatility and the opportunity to profit from it. However, as I quickly learned during
my period of paper trading this volatility would cause me to incur more losses than profits. Since
then I 've adopted a more conservative trading style which is mainly oriented around technical
analysis. However, I 'm still quite interested in the immediate and often drastic price movements
which happen when news is released. My primary goal of this paper was to understand and possibly
adopt new elements into my trading strategy to become a better trader.
Empirical framework
Setup
I will be analyzing the effect expectations have on price movements within the foreign exchange
market. In order to estimate this, a few key variables are required: price, forecasted, previous and
actual news numbers. Using these variables we can estimate the effect on which major news events
have on the price movements. Defining Yit as the variable which represents the change price in the
current period I and at time t. Yit equals 1 if there is no change in price from the mean, and equal to
1 if there is a deviation

The American National Election Studies Essay
Data The data obtained for this project has been gathered from the American National Election
Studies, better known for short as "ANES." Before continuing it is best to give some background
information of the American National Election Studies, and why they are a reliable source for
information. From the ANES website, they explain, "The mission of the American National Election
Studies is to inform explanations of election outcomes by providing data that support rich
hypothesis testing, maximize methodological excellence, measure many variables, and promote
comparisons across people, contexts, and time" (ANES). ANES is a reliable source for data because
it is used by many other social scientists. Many experiments conducted by other researchers have
relied solely on the data from the American National Election Studies. The data source that is being
used throughout this paper, are results from the American National Election Studies data file for
2012 (ANES, 2012). The ANES is an academic survey and a reputable source, which allows for
investigation into how variables such as income, race and education has an impact on party
identification. Methods As mentioned previously, this experiment would focus on three independent
variables, which are income, race and education. The dependent variables were Democrats and
Republicans, now it is time to break each variable into specifics. The first independent variable was
income. Income was originally coded into five categories. The

Applying The Two Step Testing Procedure For Asset Pricing...
In order to test the validity of the CAPM, we have applied the two–step testing procedure for asset
pricing model as proposed by Fama and Macbeth (1973) in their seminal paper. Time–series
Regression Preliminary beta estimation Using time series regression on the monthly returns we have
estimated the beta coefficient for each stock. Using the market model of CAPM i.e., regressing each
stock's monthly returns against the market index (Nifty100) we have estimated individual stock
beta's. R_it–〖R 〗_ft= α_iT+β_iT (R_mt–R_ft) +ε_it Where, Rit is the rate of return on asset i at
time t Rft is the risk free rate at time t Rmt is a rate of return on market portfolio at time t βiT is the
beta of stock I, T is the final data point εit is the random disturbance term in the regression equation.
For estimation of betas, the above equation was run for the period from Jan, 2003 to Dec, 2006.
Based on the estimated betas we have divided the sample of 63 stocks into 10 portfolios each
comprising of 6 stocks except portfolio no.1, 5 and 10 having seven stocks each. The first portfolio
1 has the 7 lowest beta stocks and the last portfolio 10 has the 7 highest beta stocks. The rationale
for forming portfolios is to reduce measurement error in the betas. Estimating Portfolio Betas We
have used the data from Jan.2007–Dec.2011 (60 data points) to re–estimate βi's which were
averaged across securities within portfolios in order to obtain 10 initial portfolio βpt using the
following regression

The Preferred Approach From The Perspective Of The Employee
4:2:3 Findings: Theme 4: The preferred approach from the perspective of the employee
To investigate the preferred approach of the ITM from employee's perspective, two types of data
were needed. One is data indicating the direct preference of the employee and two is data indicating
satisfaction with ITM being approached in that manner. To determine these two, the questionnaire
had relevant questions. The first question of interest was 11. Internal talent management is better
than external talent management. The second question of interest is question 10 which directly
addresses the issue of satisfaction as a consequence of ITM programs. Question 11. ITM programs
improve my job satisfaction levels.
To find out which approach is preferred, ... Show more content on Helpwriting.net ...
How satisfaction relates to the level of ITM present also relates to how groups are perceived. What
this means is that in order to get a better idea of how important ITM is to the company, there needed
to be data of all employees regardless of position or rank. What this means is that there is a greater
possibly of employees just being treated as employees instead of specific opinion but this too can
open doors for further evidence concerning ITM. How question 10 is perceived about satisfaction
may have little to do with rank or culture but more so how values are communicated to the
employee. As all employees only Agree or Strongly Agree which leads one to see how setting the
scale for employees only regardless of rank works better to determine the level of satisfaction as it
relates to ITM. Suppose the idea here is that with ITM being used, there is greater levels of
satisfaction at work and greater rates of people being loyal to the company. With mean scores
averaging between 52 percent and 57 percent toward Agree or Strongly Agree ranges, this validates
the idea that satisfaction is a very important result of ITM practices and strategies. How this relates
to the bigger picture only suggests more so that employees care about the level of talent being
promoted at each company and this is evident in both companies mean results. Question 10

Evaluation Of A Non Independent Variable, Dependent...
Methodology The design of the study revolves around three variables including an independent
variable, a dependent variable, and a mediation variable; where the independent variable is the
average number of hours a student studies per week, the dependent variable is the average
restfulness of student's sleep, and the mediating variable is stress reaction. It is hypothesized that the
average number of hours a student studies per week is associated to their positive sleep quality
rating, where the higher number of hours of studying will predict a lower positive sleep quality
rating. In addition, a student's stress reaction score measured by multidimensional personality
questionnaire (MPQ) standards on stress reaction factor will act as a mediator on the student's sleep
quality. A total of 278 participants aided in the formation of the data and statistics of this study. This
sample was chosen through a voluntary questionnaire given to Fall Semester 2016 Psychology300
students. It was a convenient sample to take from due to the high likelihood of most of the student's
taking the survey. The questionnaire was administered through an online survey website termed
"Qualtrics." The incentive to take this questionnaire and complete it accurately was eight points
worth of extra credit awarded to students who did so. Additionally, it was disclosed that students
would be using the survey data for later project purposes and should answer truthfully to provide
themselves with the

Ols Regression Estimates That The Return Of Schooling
OLS regression estimates that the return to schooling is 9.06% for men and 10.62% for women. The
first stage results, reported in the second column of Table1, indicate that the RoSLA to 16 in the UK
increased the average number of years of education by 0.12 for men and 0.74 for women. Our
second stage IV shows that an additional year of education increases returns to the log of wages of
13.21% and 22.39% for men and women respectively. Once again, our IV estimates are higher than
OLS which could be to a few reasons such as measurement error, causing OLS to suffer downward
bias, and LATE. In the case of both men and women there are many observable factors such as
ability and skills on the impact of the years of schooling which can also affect the individual's
earnings. Moreover, IV captures the average return of an extra year of education induced of the
compliers between the two reforms, while OLS captures average return across all people and all
years. IV is thus higher due to higher marginal return to education at relatively low education level.
Whilst I didn't run regressions for the effects of the first reform on women, previous literature has
shown that women typically had returns of around 8.3% (OLS) and 9.3% (IV) from the first reform
(Dearden, 1998). From my results, it is evident that women experience significantly greater returns
to education from the second reform than men did in both the first and second reforms. Men actually
experience a decline in the returns

My Interest Of Computer Science
My interest in Computer Science started at a young age, when I started taking lessons in BASIC in
school. As I continued my education in computer science with COBOL, C, C++ and SQL, this
interest grew into complete absorption with the subject throughout my school years. When the time
came for me to choose a major for my undergraduate studies, I confidently opted for Computer
Science which was a full–fledged passion of mine by then. During my Bachelor's, I enjoyed the
curriculum immensely, and several of the subjects I was taught like Data Structures in which Linked
List, which is a dynamic structure whose length can be increased or decreased at runtime, was my
favourite topic. Java Programming won me over when I had to do a custom FTP server 8 years ago
where the file transfer was controlled by the server instead of being controlled by client, Database
Management System taught me about normalization of database a topic I enjoyed as it is the
foundation of every database and without which the task become arduous and can lead to data loss.
Object Oriented Systems concepts like polymorphism, inheritance, encapsulation and abstraction
were fascinating. Finally, while studying Operating Systems I understood the importance of
operating systems like Unix and Linux in today 's competitive world. In order to gain thorough
knowledge of my studies, I undertook several projects during my undergraduate studies to enhance
the theoretical concepts taught in class. Library Management

Managerial Econ Chapter 5
An estimated regression coefficient is 10 with a standard error of 5. The null hypothesis is that the
partial regression coefficient equals zero. What is the value of the t–statistic for testing the null
hypothesis of the regression coefficient? Choose one answer. | a. 2 | | | b. 0.5 | | | c. 5 | | | d. 1 | |
Correct Marks for this submission: 1/1. Question 2 Marks: 1 Given the demand function in log–
linear form: Q = 120 – 1.5P + 12ADV where Q = quantity, P = price, and ADV = advertising
expenditures, what is the price elasticity? Choose one answer. | a. –1.5, elastic | | | b. 1.5, inelastic | | |
c. 12, elastic | | | d. 120, elastic | | Incorrect Marks for this submission: 0/1. Question 3 ... Show more
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As the manager of Good A, which of the following would be of greatest concern (based on the
regression results above)? Choose one answer. | a. None of the factors below would be of concern. | |
| b. pressure on you by your salespersons to lower the price so that they can boost their sales | | | c. an
impending recession | | | d. a price reduction by the makers of good B | | Incorrect Marks for this
submission: 0/1. Question 11 Marks: 1 The total sum of squares is 400 and the sum of squares errors
is 100, what is the coefficient of determination? Choose one answer. | a. 25 | | | b. 1.00 | | | c. 0 | | | d.
0.75 | | Correct Marks for this submission: 1/1. Question 12 Marks: 1 One of the series included
among the lagging indicators is Choose one answer. | a. average duration of unemployment. | | | b.
the change in sensitive material prices. | | | c. employees on non–agricultural payrolls. | | | d. the
index of industrial production. | | Correct Marks for this submission: 1/1. Question 13 Marks: 1 The
t–statistic is computed by Choose one answer. | a. dividing the regression coefficient by the standard
error of the estimate. | | | b. dividing the standard error of the coefficient by the regression
coefficient. | | | c. dividing the R2 by the

Linear Regression Test
Introduction
The study examined the relationship between helping behavior and the character traits which a
person most identifies with, selflessness or selfishness. Also, included in the study was an analysis
of the relationship between helping behavior and the number of siblings a person has. We
anticipated that there would be a correlation between the character traits, selflessness and
selfishness, and helping behavior. Thus, we expect to see a significant positive correlation
coefficient indicating there is a relationship between the character trait, selflessness and helping
behavior. We believe there will be a relationship between these two variables because customarily
people who express the character traits, selflessness or selfishness, ... Show more content on
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There was a total of 26 participants in the study of which the majority were female (65.40%) and
males only made up (34.60%). The group consisted of participants with ages ranging from 18–24.
Of the 26 participants, the overall average age was 20 (M = 19.88, SD = 1.37). The survey consisted
of three continuous outcome questions and predictor questions that were categorical or continuous.
Data from the third continuous outcome question which prompted a participant about their
willingness to help someone who was being discriminated against, was used to examine the
predicted relationships in the first part the study. Participants could choose from values on a 1 to 9
scale indicating their degree of willingness to help in a situation where a person in being
discriminated; 1 being extremely unwilling and 9 being extremely willing. Data from the continuous
predictor question, which asked a participant to score themselves based on how selfish or selfless
they perceived themselves was used to predict relationships in the second part of the study.
Participants could choose values ranging from 1 to 9 indicating which character trait they most
closely identify with; 1 being extremely selfless and 9 being extremely selfless. Correlation
coefficient and linear regression tests were then run to examine the relationships between the

Mphil
1/22/07 252corr (Open this document in 'Outline' view!) L. CORRELATION 1. Simple Correlation
The simple sample correlation coefficient is [pic] or if spare parts [pic], [pic] and [pic] are available,
we can say [pic] Of course, since the coefficient of determination is [pic][pic] , [pic] and it is often
easier to compute [pic] and to give the correlation the sign of [pic] . But note that the correlation can
range from +1 to –1, while the coefficient of determination can only range from 0 to 1. Also note
that since the slope in simple regression is [pic] , [pic] or [pic] or [pic]. The last equation has a
counterpart in [pic] , where [pic] is the population correlation coefficient, so that testing [pic] is ...
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Example: [pic] applicants are rated by [pic]officers. The ranks are below. [pic]Note that if we had
complete disagreement, every applicant would have a rank sum of 10.5. [pic]. The Kendall
Coefficient of Concordance says that the degree of agreement on a zero to one scale is [pic]. To do a
test of the null hypothesis of disagreement [pic], look up [pic] in the table giving 'Critical values of
Kendall's [pic] as a Measure of Concordance' for [pic] and [pic], [pic]so that we accept the null
hypothesis of disagreement.. Example: For [pic] and [pic] we get [pic], and wish to test [pic] Since
[pic] is too large for the table, use [pic]. Using a [pic] table, look up [pic] . Since 9 is below the table
value, do not reject [pic]. 4. Multiple Correlation If [pic]is the coefficient of determination for a
regression [pic], then the square root of [pic] , [pic] is called the multiple correlation coefficient.
Note that [pic] where [pic] is the sample variance of [pic], and that for large [pic], [pic]. 5. Partial
Correlation (Optional) If [pic], its multiple correlation coefficient can be written as [pic] or [pic].
For example, in the multiple regression problem, we got three multiple correlation coefficients [pic],
[pic] and [pic] If [pic] and we compute the partial correlation of [pic]we compute [pic] , the
additional explanatory power of the third independent variable after the

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
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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

A Study On Genetic Programming
Genetic Programming GP is an evolutionary computing method that uses the principle of Darwinian
natural selection in order to create computer programs to solve a problem. Koza introduced GP
(1992) as a branch of genetic algorithm. GP covers a high level of diversity by breeding random
different computer program as the population. The programs are structured like trees containing
functions and terminals. A function set and a terminal set should be defined as the sources of
functions and terminals. If they are rich enough, tree structures are able to reach any complexity.
The function set, for example, can be built of arithmetic operation (+, −, ×, or ÷), Boolean logic
functions (AND, OR, NOT, etc), mathematical functions (sin, cos, log), conditional functions (IF,
THEN, ELSE), or any other functions. The terminal set can be included of variables, numerical
constants, functions with no argument, etc. Random functions and terminals form a treelike structure
program. Branches that contain functions and terminals are connected to the root point. A typical
example of GP tree is shown in (FIG) Defining terminal and function sets actually determine the
search space. GP starts a blind search for solution by creating random initial population within the
search area. GP measure the fitness of each individual and the ones with better fitness are chosen for
building the next generation by mutation, cross over or direct reproduction [1]. During the
reproduction, some of the population

The Methods Of Collecting Data
Research Method Task 1 In present day, to do one research requires several big steps. These big
steps contains with research question (hypothesis), data collection, and data analysis, also with the
characteristics of errors avoidance (Gerrard, 2014). There are several kinds of research, the main
kinds of research such as Academic research, Real–world research, and Laboratory research. This
essay will focus on Management research, also will be explained the results of analysis the methods
that used in twelve management research journals, and compare the patterns that have been used in
those journals. First point that will make in this essay is about the methods of collecting data. To
gain the results and conclusion for hypotheses, there ... Show more content on Helpwriting.net ...
From this result, it illustrates that most of research have adopted quantitative data to analyze the
hypothesis. Quantitative research should have fixed planning design before collecting data, while
Qualitative research are more flexible and could change the procedures once the project begins
(Gerrard, 2014). From the article, the methods that these journals apply for collecting quantitative
data, most of the journals used database that collected from certain organizations, and conducted
survey from participants to be their main sources. For qualitative data, in this article the ways that
these journals using for data collection were interview and observation of the participants. Second
point is about the methodology that these journals used to analyze the data after the collection to
find out the results of the research. The similarities of the journals that have been found, almost all
of the journals that use quantitative data for analysis were using descriptive statistic and correlation.
Descriptive statistics are also known as "Summary statistics" there are the approaches of
summarization of a set of data. Also descriptive statistics separate into two measures, which are
measures of central of central tendency, and measures of variability. In term of correlation,
correlation coefficients are the method to explain the relationship between two variables and also
express the strength in their

Regression and Hypothesis Testing
As discussed in the Module 5 DQ 1, the most vital function of the hypothesis testing is in researches
where the needs to be a conclusion drawn from a logical approach of making a claim and proving
that the claim is rejected or not with respect to samples and respective statistical approaches. The
claim could be for the effect on blood pressure with certain hypertensive drugs as Beta–blockers, the
adverse effects of certain anti–cancer drug in comparison to another anti– cancer drug, the claim that
fast foods cause heart diseases in contrast to healthy, the claim that alcohol and drunk driving are the
major cause of accidents, etc. The hypothesis testing is taken from a normally distributed population
with the known mean and known or unknown standard deviations; the claim could also be
conducted about the standard deviations or variances. It is normally concerned with drawing
sensible inferences and are not associated with making prediction from the values drawn from the
variables.
The regression on the other hand is probably the most used statistical procedure in public health and
beyond (example, business, law, administrative area, banks, etc). Regression normally utilizes more
than one variable to predict the value of one variable in regards to the other. It uses the related
variables to construct the behavior of the taken variable. The linear correlation which is represented
as r is a number that can be achieved by using a scatter plot to draw a graph and an equation

Examples Of Objective Anxiety
Objective and Neurotic Anxiety:
There are two kinds of anxieties, the objective anxiety and the neurotic anxiety. Objective anxiety
occurs when there is a real, external threat and danger and neurotic anxiety arrives from within the
self, due to unconscious feelings and memories. In order for the individual to cope with the neurotic
anxiety he develops various defence mechanisms. One of them is the mechanism of projection
which is when his unwanted feelings and impulses that are responsible for the anxiety, are projected
to someone else. This has as a result, the individual to alleviate his anxiety but by acting in this way
the real threat is still within himself and it still influence him by distorting his reality. This distortion
of the reality is the effect of his projection as now he is perceiving others as not what they really are
but as what he had projected onto them. Others can introject, absorb and identify with these
projections and start to act and behave like they belong to them. This is what Melanie Klein named
Projective identification.
Social Defences against Anxiety: It can be said that Groups also function in that ... Show more
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That means that the reality of the organisation can be distorted but also the members will act and
behave inefficiently and in a way which is not characteristic of their true self. In Menzies Hospital
case study, for example, objective anxiety was provoked due to the nature of the task that the nurses
had to deal with. The nature of their work was itself "disgusting" and "frightening" and they had to
deal with "life and death matters". This objective situation led to neurotic anxiety as it had the
capacity to "...arouses strong libidinal and erotic wishes and impulses" which belong to the
individual's unconscious inner world and

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 (R² and
Adjusted R²). 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 on Helpwriting.net ...
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 < α = 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
Coefficients Standard Error t Stat P–value Intercept 0.44670314 0.048999234 9.116533063
2.24627E–18 Year_x_Natural_gas 0.438448124 0.181073748 2.421378744 0.015838578
30year_x_Gold –0.355632988 0.092061454 –3.862995565 0.000127684
30year_acc1_x_Copper_vel4 0.375280092 0.092056258 4.076638553 5.36313E–05
30year_acc1_x_West_Texas_vel4 –0.244658773 0.08932177 –2.739072154 0.00639495
Aluminium_vel2_x_Copper_vel1 –0.1477128 0.059326221 –2.489840022 0.013123889
Baltic_vel4_x_West_Texas_acc2 0.237141763 0.068276655 3.473248129 0.000561658
Copper_vel3_x_HFC_acc2 –0.396940325 0.081145963 –4.891682959 1.37459E–06
Copper_vel3_x_SPDR_XOP 0.174864153 0.08329485 2.099339318 0.036318588
Copper_vel3_x_West_Texas_vel2 0.320439024 0.084382283 3.797468064 0.000165301

Analysis Of Bwi's Ad Campaign
In utilizing hypothesis testing, BWI's ad campaign that states the average airfares from BWI are
lower than averages fares from Dulles. The average (one–way) fares from Dulles over comparable
routes is claimed to be $165. If there is a 10% level of significance with over 100 degrees of
freedom, there is evidence that the mean sale price is actually not less than those at Dulles. In Figure
1, the t score equals –1.47. Therefore, the null must be rejected because the absolute value of T
(1.47) is greater than T of alpha (1.28) for the upper–tail test. The Dulles average fare of $165 is
greater than the average fares of BWI of $158.33 (from Figure 2). Average fares at BWI are actually
less than the fares at Dulles. Thus, because the null is rejected, there would be a Type I error. If the
null is rejected, but BWI accepts the null as true, then the Type I error would prove problematic. The
univariate estimate of fares in question previous aforementioned in the above statement, also known
as the point estimate method, can be improved by using another common type of univariate
estimation technique. In the point–estimate approach, we randomly choose a fare that would fall
under the range most of the fares in the data set fell under. Yet, the interval estimate method is more
accurate to use because point–estimate is technically impossible because probability at a specific
point is always zero. So, the interval estimates for the average fare with a 90% confidence level
would be

A Report On A Cox Model Essay
A Cox model is a well–recognized statistical technique for exploring the relationship between the
survival of a patient and several explanatory variables. A Cox model provides an estimate of the
treatment effect on survival after adjustment for other explanatory variables. It allows us to estimate
the hazard (or risk) of death, or other event of interest, for individuals, given their prognostic
variables. Interpreting a Cox model involves examining the coefficients for each explanatory
variable. A positive regression coefficient for an explanatory variable means that the hazard for
patient having a high positive value on that particular variable is high. Conversely, a negative
regression coefficient implies a better prognosis for patients with higher values of that variable.
Cox's method does not assume any particular distribution for the survival times, but it rather
assumes that the effects of the different variables on survival are constant over time and are additive
in a particular scale [17]. The hazard function is the probability that an individual will experience an
event (for example, death) within a small time interval, given that the individual has survived up to
the beginning of the interval. In this model the unique effect of a per–unit increase in a variable or
covariate is assumed to be multiplicative with respect to the hazard rate [18]. For example, taking a
drug may halve one 's hazard rate for a stroke occurring, or, changing the material from which a

Evaluation Of A Correlation Analysis Essay
3.3.2.2 Correlation Analysis
The next step was to perform a correlation analysis, among the selected 25 numerical variables
(selected from PROC VARCLUS) and the 29 categorical variables. This technique is not a variable
selection technique, but rather a variable elimination method (Aggarwal, 2011) which gives the
correlation between variables. And by using the correlations, highly correlated variables can be
removed from the analysis. By the correlation analysis performed, a heat map was generated.
Thereafter, two numerical variables (Total Sales in Branch and Dairy Sales Total) were identified as
highly correlated with other variables, hence was removed from the analysis.
The code below was used to perform the correlation analysis of the data set given under 'data' and
the variable set to be considered is given in 'var' (variable list available in Appendix 3). The output
will be written to the file specified under 'out' attribute.
Figure 3.3 gives a part of the heat map generated for the correlation analysis. Compared to the other
variables, Dairy_Tot_br variable and sales_branch variable are correlated with many other variables.
Therefore, these two variables were removed from the model.
3.3.2.3 Stepwise Regression
The next step was to take a sample of the data set and to perform Stepwise Regression for 23
numerical variables and 29 categorical variables, to further reduce the number of predictor variables.
By performing stepwise regression the total number of

Definition Of Exponential Distributions
A resulting straight line through the graph of H(t) would suggest that an exponential distribution
would be the favoured choice for these data, while a straight line through the graph of log H(t)would
suggest a weibull distribution for the data. Figure~ref{HazCumHaz}, for patients who filled in a
Home Care or both a Home Care and a Contact Assessment, does not support either distributions.
Part results for outputs from the exponential and weibull distributions for these data are shown in
Appendix~ref{AppendixB}. These results confirm the evidence from Figure~ref{HazCumHaz}
and show that neither distributions fit the data well. Therefore, formal modelling for the 3525
patients who filled in either a Home Care assessment alone, or ... Show more content on
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Further, stepwise regression is also known to pick smaller models than
desired~footnote{http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch10.pdf}. The criteria used in
the step–wise variable selection process for this study was the Akaike information criterion (AIC).
AIC estimates the quality of each model, relative to each of the other models by estimating the
information lost when a model is used to represent the data. In doing so, it deals with the trade–off
between the goodness of fit of the model and the number of parameters used. When using the AIC
criterion, the model with the least is the preferred model. After using the AIC selection process, any
insignificant variables which were selected by step–wise regression were manually removed from
the model. There was also a need to consider the practicality of the model. Some highly significant
variables resulted in coefficients that were very small for any practical use. For instance, if a
variable resulted in a coefficient of $0.04$ then this coefficient would be $0.04 times$ the baseline
hazard. This would consequently increase mortality by a factor of $e^{0.04}$ days; which is $~1.04
times$ baseline number of days. The resulting increase is very insignificant and could be ignored.
Table~ref{LowCoeffs} consists of 63 variables that were removed from

In a few regions of psychology (especially in...
In a few regions of psychology (especially in psychodynamic theory), psychiatrists discuss "defense
mechanisms," or way where we behave or have confidence in a couple of manners to better shield or
"protect" ourselves. Defense mechanisms are only one way of looking at how people distance
themselves in the total familiarity with disagreeable feelings, thoughts and behaviors. A Mental
Defense A Mental Defense A Mental Defense Defense mechanisms have been categorized by
shrinks based upon how straightforward they are. Yet, more straightforward defense mechanisms are
typically quite successful short–term, and therefore are favored by a lot of folks and children
especially (when such straightforward defense mechanisms are first learned). ... Show more content
on Helpwriting.net ...
When may be regressed through an adult below a lot of striving, refusing to depart their bed and
participate in routine, routine jobs. 3. Acting Out To be able to express thoughts or feelings the guy
feels unable of otherwise expressing acting Out is performing an extreme conduct. As an alternative
to saying, "I am disturbed with your actions," a guy who acts out may instead throw a publication in
the person, or punch a hole through a wall. For instance, a child's temper tantrum is a type of acting
out when he or even she will not get their way having a parent. 4. Dissociation Dissociation is when
someone loses track of time and/or guy, and instead finds another representation of the self to be
able to continue in now. Somebody who dissociates often loses course of time or themselves along
with their ordinary thought processes and memories. People who get a history of any form of youth
maltreatment often suffer from some type of dissociation. In remarkable cases, dissociation can
cause someone contemplating they have many selves ("multiple personality disorder"). People using
dissociation normally have a disconnected view of themselves inside their own universe. Time as
well as their unique self–image as it does for many folks, might not stream nonstop. This way,
somebody who dissociates can "disconnect" from real life to get a span, and live in an alternative
universe that is not littered with thoughts, feelings or memories that are intolerable.
Compartmentalize

Test Anxiety and Student Performance Essay example
Test Anxiety and Student Performance
Abstract
Test anxiety is a real and measureable problem student's face regardless of their grade or level of
academic achievement. Test anxiety can also adversely affect how students participate in and view
the learning process long term. This study was designed to examine the effects of test anxiety on
high school students specifically, and how the stress associated with the processes or outcomes of
standardized testing can negatively impact their performance. There is also a theory that
contributing factors of test anxiety can also impact social anxiety. The purpose of this study is to
determine if students with determined higher levels of test anxiety perform lower overall on
standardized ... Show more content on Helpwriting.net ...
They tend to engage in distracting and negative thoughts about themselves, which prevent them
from concentrating on their tasks (Zatz & Chassin, 1985). As a result, these students have
difficulties learning and excelling on standardized tests. However, it is important to highlight the
fact that any students, regardless of their level of intelligence or academic performance, can be
adversely affected by test anxiety (Clovis, 1999). Most significantly, Clovis (1999) pointed out that
these inappropriate strategies can trigger further increases in test anxiety and create a cycle of
academic failure. Preliminary Literature Review
To address this phenomenon, researchers have proposed different theories of test anxiety to account
for the effects of test anxiety on the deficits of academic performance. According to scholars such as
Schmidt and Riniolo (1999), the cognitive aspects of test anxiety – worry and task–relevant thinking
– are also present in social anxiety. Therefore, students who experience test anxiety may also suffer
from other types of psychological and cognitive problems such as self–esteem, cognitive
development, social skills and memory. Essentially, the students who suffer from test anxiety are
individuals who are unable to cope with any types of stress. Considering the stressful nature of

Correlation Between Underpricing Using First Day Return
To test the correlation between underpricing using first day return as dependent variable and all
other variables as independent variables, we have run a pair–wise correlation. The matrix shows that
only auditor dummy and ebitda before have a positive correlation with the first day return. However,
with the p–value to test the significant level of the result, all independent variables generated have
relatively large p–values. In this case, they are tested to be statistically insignificant to explain the
underpricing except share outstanding has weaker correlation to the underpricing. Table 1: Pair–wise
correlation matrix on IPO Adding a new variable–ln book value, which is a symbol to measure the
size of the issuer, we can run a ... Show more content on Helpwriting.net ...
It is positively skewed. Graph 2: Kernel density estimate distribution To test the multicollinearity,
we use the VIF test. From the table it is obvious that no variables have a VIF which is greater than
10. That is, no trouble variables contribute to the multicollinearity problem. Table 3: Test for
multicollinearity (IPO) By generating a new variable–underpricing dummy, we can use the dummy
variable to run a logit model for comparison with the standard OLS regression. The result
persistently indicates a correlation between underpricing and ln book value. In addition, positive
ebitda before now is likely to explain the underpricing with a far smaller p–value and a higher z–
statistics to state its significance. Table 4: Logit model using underpricing dummy Week 7 In order
to examine the relationship between antis takeover provisions (ATPs) in US acquiring corporations
and their decision makings. We utilizer the data provided to replicate the model in Masulis, Wang
and Xie (2008)' study and the result is as follows. By summarizing the data, we can interpret that the
cars (cumulative abnormal return) has a mean of 0.313% with a standard deviation of 4.358%.
Meanwhile, the gim, bcf, lnta and tv variables have high standard deviation compared to other
variables. To further study about the gim and bcf, we have run the

4.03 Linear Regression
Using MINITAB perform the regression and correlation analysis for the data on CREDIT
BALANCE (Y) and SIZE (X) by answering the following.
Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line.
Interpret.
The scatter plot of Credit balance ($) versus Size show that the slope of the 'best fit' line is upward
(positive); this indicates that Credit balance varies directly with Size. As Size increases, Credit
Balance also increases vice versa.
MINITAB OUTPUT:
Regression Analysis: Credit Balance($) versus Size
The regression equation is
Credit Balance($) = 2591 + 403 Size
Predictor Coef SE Coef T P
Constant 2591.4 195.1 13.29 0.000
Size 403.22 50.95 7.91 0.000
S = 620.162 R–Sq = 56.6% R–Sq(adj) = 55.7%
Analysis of Variance
Source DF SS MS F P
Regression 1 24092210 24092210 62.64 0.000
Residual Error 48 18460853 384601
Total 49 42553062
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI 1 4607.5 119.0 (4368.2, 4846.9) (3337.9, 5877.2)
Values of Predictors for New Observations
New Obs Size 1 5.00
Determine the equation of the "best fit" line, which describes the relationship between CREDIT
BALANCE and SIZE.

The equation of the "best fit" line help describes the relationship between Credit Balance and Size is
Credit Balance ($) = 2591 + 403.2 Size
Determine the coefficient of correlation. Interpret.
The coefficient of correlation is given as r = 0.752. The

Testing As A Service Provider
Testing as a Service @ Optum Global Solutions Introduction: Testing as a Service (TaaS) is an
outsourcing model in which testing activities associated with some of an organization's business
activities are performed by a service provider rather than employees. TaaS may involve engaging
consultants to help and advice employees or simply outsourcing an area of testing to a service
provider. Usually, a company will still do some testing in–house. TaaS is most suitable for
specialized testing efforts that don't require a lot of in–depth knowledge of the design or the system.
Services that are well–suited for the TaaS model include automated regression testing, performance
tests, security testing. TaaS is also sometimes known as on–demand ... Show more content on
Helpwriting.net ...
And some companies cannot afford to invest in best–of–breed solutions when testing and
automation is not their core competency. One model used to handle specialized test efforts is to
augment the staff with highly–skilled consultants. Another option is to use "Managed Service"
solutions where organizations pay for full–service testing efforts, paying for the people resources
needed during the period of time when specialized skills are necessary. "As a Service" solutions are
becoming a popular way for organizations to get a service, without the investments or risks
necessary by doing the work in–house. Often taking advantage of cloud computing and utility
pricing, this model allows organizations to buy what they need, when they need it. Testing as a
Service (TaaS) solutions are popular for complex testing efforts requiring a specialized skill set,
expensive testing tools or a large amount of resources. When would you want to use TaaS? Certainly
TaaS will not cover all of your testing needs. Regardless of test efforts that are outsourced, a good
amount of testing should be done in–house when a project is under development. Unit tests,
functional tests, and integration tests that require knowledge of the system should be performed
throughout the lifecycle with a strong partnership between developers and testers. However, for
tasks that can be automated, but require a specialized skill set or tools, it may be best to use an
outsourced solution rather than try to hire

Oil and Dutch Disease
ECONOMICS FOR BUSINESS
Project Report on –
Oil and the recent
‟Dutch Disease‟
– The Case of the United Arab
Emirates
Submitted by –
Amitava Manna
1|Page
Table of Contents
Introduction
.................................................................................................................................................. 2
Purpose
......................................................................................................................................................... 3
UAE Background
........................................................................................................................................... 4
Theoretical Framework ... Show more content on Helpwriting.net ...
Purpose
The purpose of this paper is to study U.A.E‟s development in economic growth since 1975 and
establish if there are any signs of the Dutch Disease by testing the ratio of tradable goods to non–
tradable goods and the effects by other macroeconomic variables.
3|Page
UAE Background
U.A.E consists of the seven emirates Abu Dhabi, Dubai, Sharjah, Ra´s al–Khaimah, Ajman,
Umm al–Qaiwain and Fujairah, which are located on the southern Arabian Gulf. On the 2nd of
December 1971, the country became independent after being under British rule for a period of 70
years. The independence and discovery of oil triggered the economic development in U.A.E which
led to a huge expansion in the population. The population boom in U.A.E is a result of the increased
demand for labor throughout the past four decades and consists for the most part (83%) of labor
from foreign countries referred to as expatriates. United Nation‟s (UN) database illustrates the
division of the labor from two perspectives; first from the year 2000 compared to the changes that
prevailed in 2010. Female participation and male participation in 2000 consisted of 34.4% in the
former group and 92% in the latter group. As stated in the introduction, one of the impacts when an
economy is experiencing signs of the Dutch Disease is the high inflation rate followed by a change
in the real exchange rate. Fluctuations in the

Sigmund Freud's Life Research Paper
Sigmund Freud's Adventure
Have you ever heard of Sigmund Freud? If not then, he is the one who is well known as the father of
Psychoanalysis. Freud was one of the modern–day psychologist, he prepared a technique for
understanding the human behavior, and the effort resulted a complete theory of personality and
psychotherapy ever developed. Freud is the firstborn in his family of three boys and five girls. He
was born in Freiberg, a rural town near Ostrau in northwestern Moravia. Freud had many interests in
his early age, because of his Jewish heritage his choices to pick a career were limited. Freud was the
one who continued to modify his theory for a long period of time. Psychoanalysis is focuses on the
unconscious aspects of personality. Freud thinks that our brain is like an iceberg, it is hidden in
unconscious. He thinks that conscious level of mind is the tip of the iceberg, which we could see,
but the unconscious was the hidden part beneath the water. Freud theories have three levels of
awareness: the conscious, the preconscious, and the unconscious. Freud also divided the personality
structure into three components id, ego, and superego, which has different principles and different
modes of thinking. Freud has another major aspect of psychoanalysis in the development of defense
mechanism. Defense mechanism are certain ways people react to frustration and conflict by
deceiving themselves by their goals and by maintain their self–esteem and to avoid anxiety. It is the

Effects Of Obesity On The United States Essay
Obesity rates in the United States have increased significantly over the past three decades, which
have led to a multitude of health problems in the nation. I tested for the effects on obesity, primarily
the effect of poverty with obesity rates. To begin with I used a single regression model to compare
our nation's GDP with the average BMI, assuming that they will be positively correlated. Next I will
attempt to draw conclusions about correlations between other factors including obesity, poverty and
education levels using the single and multiple regression model. Findings show that there is
significance between per capita income and obesity in the United States in the single regression
model at the 10% proof testing level, but using the multiple regression model showed to be
inconclusive. I. BMI, GDP and Per Capita Income BMI (Body Mass Index) is the value derived
from weight and height of an individual. Having a BMI value of 25–30 is considered overweight
and >30 is considered obese. Obesity can cause a plethora of health issues. When BMI rates rise so
does the rate of disease and disorders. Obesity can lead to health ailments like cardiovascular
disease, diabetes and various types of cancer. Obesity caused ailments are preventable. Around
300,000 deaths occur each year from obesity related issues. This makes in one of the top causes of
preventable death in the United States. In 2010 estimated cost of obesity was 190.2 billion and
accounted for nearly 21% of the annual

The Causal Effect Of Class Sizes On Educational Outcomes...
Introduction Parents and politicians alike have fixated on class size ever since the beginnings of
modern education. Conventional wisdom holds that smaller classes lead to better educational
outcomes – after all, fewer students imply greater one–on–one instructional time, and disciplining
overcrowded classrooms is arguably more difficult. Still, hiring more teachers is far from cheap, so
policymakers have a vested interest in determining the effectiveness of smaller classes. Historically,
isolating the causal effect of class sizes on educational outcomes has proven challenging. With
factors from socioeconomic standing to innate IQ affecting student learning, teasing out the effect of
class sizes is rather challenging. Although economists have executed randomized experiments in
states like Tennessee (Finn and Achilles 1990), such experiments are limited in size. Yet a paper by
Joseph Angrist and Victor Lavy suggests an alternative (and more importantly, feasible) way of
quantifying this effect.
Ancient Religion and Modern Policy
Indeed, as Angrist and Lavy note, an eight–hundred–year–old religious decree offers an unusually
effective way to separate the influence of reduced class sizes from that of factors like parental
involvement and remedial education programs. This decree, named Maimonides' Rule after the
twelfth–century Rabbinic scholar, requires "two teachers [to] be appointed [...] if there are more than
forty [students in a class]." In other words, if a local Israeli

Statistics Paper
Case Study: Condo Sales Case A. Brief introduction to the case This case involves an investigation
of the factors that affect the sale price of oceanside condominium units. The sales data were
obtained for a new oceanside condominium complex consisting of two adjacent and connecting
eight–floor buildings. The complex contains 200 units of equal size (approximately 500 square feet
each). Variables Dependent variable 1. Sale price: Y Independent variables 1. Floor height: x1 2.
Distance from elevator: x2 3. View of the ocean: x3 4. End unit: x4 5. Furniture: x5 Issues identified
To build a regression model that accurately predicts the sale price of a condominium unit sold at ...
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iii) The hypotheses of interest concern the interaction parameter β10. Specifically, H0: β10 = 0 Ha:
β10 < 0 Test statistic: t = 1.378, p–value = 0.170, respectively. The lower–tailed p–value, obtained
by dividing the two–tailed p–value in half, is 0.170/2 = 0.085. Since we are testing using α = 0.1, α
exceeds the p–value, thus we can reject H0 and conclude that the rate of change of the sale price of
the condos with distance from the elevator(x2) decreases when the view(x3) decreases ; that is x2
and x3 interact positively. Thus, it appears that the interaction term should be included in the model.
Therefore the new multiple regression model is: E(y) = 198.934 – 6.474 x1 – 5.746 x2 + 58.666 x3
– 21.678 x4 + 0.872 x12 + 0.533x22 – 0.264 x1x2 – 5.93 x1x3 + 0.92 x2x3 C. Insight for business
decision making Regression models are very useful in determination of important statistics in the
corporate world. For instance, multiple regression models can be used to determine whether
advertising, product loyalty, or price is the most important determinant of business growth. With this
information, businesses are able to focus their resources into the channels which will help them
achieve their targets effectively. It can also be used to calculate the predicted mileage for a vehicle
with respect to different possible variables such as weight of vehicle, age of vehicle, and climate of
country. Car manufacturers can capitalize on

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