Buad 310 Case Analysis Instruction - 1395 Words

BUAD 310 Spring 2013 Case Due by 4PM on Friday, May 3rd (in BRI 400C) In this case you will apply statistical techniques learned in the Regression part of BUAD 310. Please read the following instructions carefully before you start: †¢ This assignment uses data from the file MagAds13S.XLS, which you can download from Blackboard. After you download the file go to Data → Load data → from file in StatCrunch to open it (you don’t need to change any of the options when loading this data.) †¢ The entire report should be typed and clearly presented without typos and grammatical errors. Copy and paste the relevant (explained further in more detail) regression output into your document. Do not attach any graphs.†¦show more content†¦b. Evaluate the regression assumptions by assessing the residual plot. c. Examine each of the explanatory variables individually to determine which are contributing significantly to the model. (Use the significance level of 5 %. Do NOT actually eliminate any variables from the regression at this stage.) d. Using the same model with all the variables, provide an appropriate 95%-level interval to the retail clothing company for the amount that they would pay for a full-page ad in a magazine with a projected audience of 2,000,000 readers, 55 percent of which are male, with a median income of $30,000. Explain in one sentence and in simple terms what this interval means. 3. Rerun the regression in part 2 with circ replaced by LN_circ (the natural logarithm of the variable circ), keeping all the other variables the same. Include the relevant regression output (only the coefficient and ANOVA tables). [Stat ( Regression ( Multiple Linear, then fill in the proper Response and Predictor variables, then click Next twice and under Save options select Residuals and Predicted values. Produce a residual plot the same way as in question 2]. a. How does this model compare to the previous model using R-squared? Explain what this difference in the R-squared values means in simple terms. b. Evaluate the regression assumptions by assessing the residual