Regression Case

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------------------------------------------------- REYEM AFFAIR Regression Case Quantitative Methods II To ------------------------------------------------- Prof. Arnab Basu On October 21, 2011 By GROUP NO. 5 Bharati vishal (11110) akshay ram (11110) dhanashree vinayak shirodkar (11110) amol devnath kumbhare (11110) ajusal sugathan (11110) arun prabu (11110) ghule nilesh vishnu (11110) mudavath swetha (11110) Raja Simon J (1111052) sagar behera (11110) shreya sethi (11110) swati murarka (11110) Indian Institute Of Management, Bangalore Table of Contents S.No | Particulars | Pages | 1. | Executive Summary | 3-4 | 2. | Understanding of the Problem | 4 | 3. | Model Description | 5-13 | |…show more content…
Hence selling price is clearly the dependent variable ‘Y’ for the regression model. Clearly first date, close date and number of days between the two (Days) cannot be part of the independent variable set since we do not have these information for the 236 Ellery Steet Condominium yet (since the sale has not taken place yet). Further the condominium of interest lies in area M (9), hence one could possibly analyze only the data on the 111 condominiums from the same area and ignore the rest. On the other hand, if we can set up independent dummy variables for the area/area codes, these can be incorporated into our regression model and then we will have a bigger sample of 456 data-points to make a better and more accurate prediction for Affiar. This will be explained in detail in the model description. Stepwise regression in SPSS has been adopted for variable selection. This method, being a combination of forward selection and backward elimination techniques for variable selection, avoids the errors in regression model that can be committed due to multi-collinearity. Figure 11.45 from Pg 571 Understanding of the Problem Selection of independent variables is the key to arriving at a good regression model. On first look at the given data, one can clearly see that the possible independent variables that may be affecting the selling price could be first price, last price, number of days between first and last date, location (Area), number of bedrooms, number of
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