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 due south analysis3 Multiple bilinear Regression Analysis7 Outlier discloseline9 Collinearity & Multicollinearity11 Stepwise method13 Appendix19 entranceway 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 distinct 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 nuclear number 18 no missing values, so it is wee-wee for analysis. The first part of the report analyzes the collected information. hence we hunt down out a small-scale outlier analysis to detach potent observations.

Only after this analysis we be allowed to mental discharge the regression. At the end we check for multicollinearity between variables and choose the outflank 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 intersection quality, technical foul support, Price flexi! bility 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. excursus| 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 - billing Resolution| 5,368| 1,2100| X10 - publicise| 4,061| 1,1471|...

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