Please consider the data presented above for the monthly sales of Ever-cool brand of refrigerators in 1,000s of dollars (Dependent Variable) and the four independent variables. Independent variables are: Price (in dollars); Promotional Expenditure (in 1,000s of dollars); Quality of service (scale of 1-100); location (categorical variable: city area: 1; suburban area: 0). Develop a multiple linear regression equation using either Excel or Minitab and based on the relevant outputs please answer the following questions: a. Based on the relevant residual and normality plots, do you see any evidence of violation of assumptions (Linearity, Normality, Equal variance)? You must attach the relevant plots as part of your report. b. State the multiple regression equation and interpret the statistical meaning of the estimated slopes, b₁, bz, ba, and be (corresponding to the four independent variables). c. At the 0.05 level of significance, determine whether each independent variable makes a significant contribution to the regression model. Based on these results, indicate the independent variable(s) to include in this model. (Based on t-test results) d. Construct a 95% confidence interval estimate of the population slope between the independent variable 'Quality' and the dependent variable 'Monthly Sales' (B) (please note that Minitab can't do this directly, however you may use the relevant information from Minitab output and then construct the confidence interval manually) e. Perform the overall F-test and comment on the significance of the model. Use Excel to run the analysis. Promotional Exp Price($) Quality City: 1/Suburban: 0 | Sales(K) (K) 949 5 100 1 168 941 4.3 94 1 150 934 3 89 1 168 921 2 85 1 148 915 0.75 79 1 152 909 4.8 75 1 162 904 3.6 70 1 160 1014 3 63 0 123 1006 1.5 60 0 130 990 0.7 55 0 116 978 4.7 51 1 142 962 3.5 47 1 145 955 2.8 42 1 134 953 1.3 35 0 128 1050 0.25 30 0 117 1040 4.5 26 0 118 1038 3.2 22 0 107 1022 2.4 17 0 124 1021 1.2 12 0 104 1018 0 6 0 106 1010 2.9 60 0 120 935 4.4 91 1 153