Quantile regression. The Journal of Economic Perspectives
This paper is formulated towards that of regression analysis use in the business world. The article used for this paper was written in order to understand the meaning of regression as a measurement tool and how the tool uses past business data for the purpose of future business economics. The research mentioned in this article pertained to quantile regression, or how percentiles of specific data are used in estimating that of future possibilities in data. The hypothesis to the study of quantile regression showed that regression analysis in the business world may not give a complete picture of how data is distributed, but rather a curve or educated guess. The main findings of the study where used in order to persuade those involved in economic development to continue exploring the means of refining quantile regression so that it can be used as an extensive strategy in completing that of regression in the business world. The gender pay gap in Vietnam, 1993–2002: A quantile regression approach The purpose of the study is to investigate the gender pay gap between men and woman between 1993 and 2002 in Vietnam. The research questions where what is causing the gap and why are men paid significantly more than women. The hypothesis of the study is a “glass ceiling” effect the reason why the women are paid so little. The main findings of the study are women in some instances make more money at being self-employed. There are no specific reasons for the gap but since 2002 it has been closing gradually.

Ordinal Regression Analysis
The purpose of an ordinal regression analysis is to determine the difference and significance of existing ordinal values on a scale that ranges from low to high inclusive of below average, average, and above average. Which Presidential Candidate would be the best person for the United States? Which method of medical treatment is the best cure...

...STA9708
RegressionAnalysis: Literacy rates and Poverty rates
As we are aware, poverty rate serve as an indicator for a number of causes in the world. Poverty rates are linked with infant mortality, education, child labor and crime etc. In this project, I will apply the regressionanalysis learned in the Statistics course to study the relationship between literacy rates and poverty rates among different states in USA. In my study, the poverty rates will be the independent variable (x) and literacy rates will be the dependent variable (y). The purpose of this regression is to determine if there is a correlation between the poverty rates and literacy rates in different states within USA. My null and alternate hypothesis are as follows:
Null hypothesis: Ho: β1 = 0 This hypothesis states that there is no correlation between the literacy and poverty rates
Alternate hypothesis: Ha: β1≠0 This is the hypothesis we want to prove, there is correlation between the literacy rate and poverty rates
The first step I did was to create a scatter plot for the data and the descriptive statistics study. The scatter plot shows a positive correlation between the two variables and the equation of the line is y = 1.0998x + 2.2613 with a R-square value of 0.5305. The scatter plot is shown below:
Figure 1: Scatter plot of relationship between poverty and literacy rates
Based on the coefficient of determination of 0.53,...

...RegressionAnalysis: Predicting for Detroit Tigers Game
Managerial Economics
BSNS 6130
December 13, 2012
By:
Morgan Thomas
Chad Goodrich
Jake Dodson
Austin Burris
Brittany Lutz
Abstract
As there are many who invest in athletic events, the ability to better predict attendance to such events, such as the Detroit Tigers games, could benefit many. The benefits include being able to better stock concessions stands, allocate advertising budgets, and staff security. Therefore, the aim of our study was better explain the variation in attendance to the Detroit Tigers home games. The variables we used included:
* Opponent
* Detroit Tigers’ Winning Percentage
* Opponent Winning Percentage
* Ace Pitcher
* Temperature
* Promotional Days
We predicted each variable would have a positive relationship with attendance. However, our data proved that to be otherwise as you can see in our regression model:
Attendance = 29697.22 + 3524.993 (O) – 6723.86 (DTW%) + 691.2616 (OW%) + 936.4279 (AP) + 121.5826 (T) + 1810.263 (PD)
All variables except the opponents winning percentage had a positive relationship with attendance to each game. The R Squared also demonstrated that the variables used only explained 26.4% of the variation in attendance. Furthermore, P-Value of the F-Stat proved our model 99.9% level of confidence, meaning it is significant. Overall, three of our variables including temperature,...

...Quick Stab Collection Agency: A RegressionAnalysis
Gerald P. Ifurung
04/11/2011
Keller School of Management
Executive Summary
Every portfolio has a set of delinquent customers who do not make their payments on time.
The financial institution has to undertake collection activities on these customers to recover the
amounts due. A lot of collection resources are wasted on customers who are difficult or
impossible to recover. Predictive analytics can help optimize the allocation of collection
resources by identifying the most effective collection agencies, contact strategies, legal actions
and other strategies to each customer, thus significantly increasing recovery at the same time
reducing collection costs. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data involves the application of regressionanalysis. Based on the calculated value of correlation coefficient, there is no relationship between the size of the bill and the number of days to collect.
.
Introduction
The author was hired by the Quick Stab Collection Agency (QSCA) on a contractual basis to assist the company in auditing potential business in buying the rights to collect debts from its original owners. QSCA is a collection...

...MULTIPLE REGRESSIONANALYSIS USING DUMMY VARIABLE
HDI Regression Using Health, Education &Income
3/21/2012
Department Of Business Economics
Jasmine Kaur(598)
Kshama (577)
Maanya Kaushik
ShikhaChaurasia(600)
ABSTRACT
In this project we have employed tools of empirical econometric analysis to examine the relationship between the Human Development Index and the indicators of Human Development.
Table of contents
Topics | Page no: |
1.Abstract | (i) |
2. Literature Review | |
3. Theory 3.1 Data 3.2 Dummy Variable 3.3 Regression3.4 Interpretation | |
4. Hypothesis testing | |
LITERATURE REVIEW
Human development plays a fundamental role and remains the most important factor in Economic growth and development in countries of the world. The Human Development Index (HDI), first introduced in the 1990 Human Development Report (UNDP: 1990), was in response to the need for a measure that could better represent human achievements in several basic capabilities. This a composite statistic used to rank countries by level of “human development” and to separate countries into developed (high development), developing (middle development), and underdevelopment (low development) categories. The statistic is computed using data on Life Expectancy, Education and Per Capita GDP, each as an indicator of Standard of Living. Human Development is...

...
A. DETERMINE IF BLOOD FLOW CAN PREDICT ARTIRIAL OXYGEN.
1. Always start with scatter plot to see if the data is linear (i.e. if the relationship between y and x is linear). Next perform residual analysis and test for violation of assumptions. (Let y = arterial oxygen and x = blood flow).
twoway (scatter y x) (lfit y x)
regress y x
rvpplot x
2. Since regression diagnostics failed, we transform our data.
Ratio transformation was used to generate the dependent variable and reciprocal transformation was used to generate the independent variable.
3. Check if the model is adequate by checking the t-statistic, R2 and F-statistic.
F statistic reveals that the equation used to determine the relationship between the x and y is functional. Using the test statistic for the test of coefficients, it was revealed that the constant value in the equation is not significantly different from 0. Also, it was revealed that the transformed x, significantly explains the dependent variable. Also, it was revealed that the measure of proportion of variability explained by the fitted value is relatively high with 96.23%. This means that transformed data in blood flow explains 96.23% of the variation in the transformed data in arterial oxygen.
4. Check the normality of residuals and equal variances
predict r, resid
kdensity r, normal
pnorm tx
qnorm tx
rvpplot tx
Before we could perform the numerical test, we must first generate...

...CWRU
Regression Project Report
OPRE 433
Tianao Zhang 12/5/2011
Introduction
According to the data I’ve received, there are 6578 observations. The data base is composed by 13 columns and 506 rows. All the explanatory variables are continuous as well as the dependent variable and there are no categorical variables. My goal is to build a regression model to predict the average of Y or particular Y by a given X. 1. Do the regression assumptions such as Constant Variance, Normality and Independence and the correct functional hold for the model? By performing residual analysis, I can test the model. 2. Is there any relationship between the explanatory variables? I do multicollinearity test to test this condition. 3. I want to find out the confidence interval and prediction interval for the average Y and particular Y value. 4. In order to check the usefulness of the model and the relationship between X and Y, I consider several variables: i. Multiple Coefficient of Determination R2 and Radj2) ii. DWT iii. F Ratio iv. VIF value v. P Probability value.
Method of analysis
1. Find the important variables Use “Stepwise” to eliminate unimportant independent variables. Analysis—Fit Model—Stepwise After using “Stepwise”, JMP shows me that column 3 and column 7 should be deleted. So the rest of the columns have strong relationship with the dependent variables. 2. Checking VIF value If some...

...Home Depot Inc: Senior Management Report
Data was collected from CRSP daily observations for Home Depot starting January 1993 and ending December 2004. Observations for S&P and Home Depot were matched, and also for the T-Bill composite which is used as a substitute for the risk free rate. No unusual data patterns were observed during the work-up. After having done the Event Check, no large differences in the slopes of the data in the periods before and after 911 were discovered and both periods are used. The data matches the usual modeling assumptions and thus, results are to be expected to be interpreted without contradictions.
HD' Market Rating Analysis (MRA):
Jensen's Alpha ( ) was largely in the positive range. Therefore, Home Depot return was greater than the S&P return and HD outperformed the market. Beta ( ) was in the range of [1,1275959 to 1,0648879] and thus contained 1,0. So HD's risk/return profile is not much different from the market. (See appendix, p. 1)
The Market SPI was higher than the HD's Sharpe ratio, and therefore the market return is higher relative to total risk and the investor is better compensated for risk taken. On the other side, the TPI of the market was lower than Home Depots' Treynor ratio and thus, the company gives higher return relative to non-diversifiable risk (See Appendix, p. 1).
The BH-L Relative Unique Risk (RUR) was 47,8% which offers a moderate return for investors for risk taking (See Appendix, p. 2).
For...