Automobiles Impact

HBAT Regression analysis|
Arpine Papyan Kristine Sahakyan Naira Gasparyan Lilit Yeghoyan Mariam Chibukhchyan|
Instructor: A. Tashchian|

American University of Armenia
2011

Contents
Introduction2
entropy Analysis3
Multiple Linear Regression Analysis7
Outlier discloseline9
Collinearity & Multicollinearity11
Stepwise method13
Appendix19

INTRODUCTION

For finding out the level of satisfaction of HBAT customers Multiple Linear Regression has been used. be has been carried out among 200 customers, who answered 23 different questions. Out of the 23 variables, 13 numeric variables were chosen as independent variables for the regression toward the mean analysis, and Satisfaction was chosen as the dependent variable.
In the information collected there atomic number 18 no missing values, so it is ready for analysis.
The first part of the report analyzes the collected data. hence we carry out a small-scale outlier analysis to detach influential observations.



Only after this analysis we are allowed to mental test the regression. At the end we check for multicollinearity between variables and choose the outgo regression model for analyzing the satisfaction level of HBAT customers.

Data Analysis
The HBAT survey resulted in 200 observations available for analysis. There are 13 independent variables such as mathematical product quality, technical foul support, Price flexibility and others that influence on the dependent variable, client satisfaction.
We can become acquainted with the data using the descriptive statistics.
Descriptive Statistics|
Variable| Mean| Std. Deviation|
X19 - Satisfaction| 6,952| 1,2411|
X6 - Product Quality| 7,894| 1,3830|
X7 - E-Commerce| 3,765| ,7689|
X8 - Technical Support| 5,243| 1,6552|
X9 - Complaint Resolution| 5,368| 1,2100|
X10 - publicise| 4,061| 1,1471|...



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