Automobile Industry Structure In 1998

HBAT Regression outline| Arpine Papyan Kristine Sahakyan Naira Gasparyan Lilit Yeghoyan Mariam Chibukhchyan| Instructor: A. Tashchian| Ameri bottomland University of Armenia 2011 Contents Introduction2 selective information Analysis3 double Linear Regression Analysis7 Outlier psychoanalysis9 Collinearity & Multicollinearity11 Stepwise method13 App extirpateix19 entryway For finding fall out the level of satisfaction of HBAT customers Multiple Linear Regression has been used. espouse has been carried out among 200 customers, who answered 23 different questions. Out of the 23 variables, 13 numeric variables were elect as mugwump variables for the degeneration analysis, and Satisfaction was chosen as the dependent variable . In the data collected there are no missing values, so it is ready for analysis. The first array of the report analyzes the collected data. whence we carry out a small-scale outlier analysis to do away with influential observations.

exclusively after this analysis we are allowed to leave the regression. At the end we check for multicollinearity between variables and choose the trump regression modelling 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, technological support, bell flexibility and others that inf luence on the dependent variable, client sat! isfaction. 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 fiber| 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 - advertizing| 4,061| 1,1471|...If you regard to get a full essay, order it on our website: OrderEssay.net

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