Bob is a statistic textbook author and aspiring photographer who sells his 13x19 prints on consignment at island art in stone Harbor, New Jersey. To improve his inventory management, Bob would like to develop a model to predict the number of prints he will sell in a week. The picture provided the following data from a random selections of summer weeks over the past few years: number of prints sold per week, the price of the prints during the week, the number of prints in inventory at the start of the week, and the season (in-season or off-season). (Using Excel) (I have tried to attach the excel sheet, but it will not let me, I have added the picture of the numbers)

Questions:

a. Construct a regression model to predict the average demand for prints during the week using all three independent variable.

b. Interpret the meaning of the regression coefficients from part a.

c. Test the significance of the overall regression model from part a using ? =0.05.

d. Show the calculation for the adjusted multiple coefficient of determination for part a.

e. Using p-values, identify which independent variables are significant from the model in part a with ?= 0.05.

f. Using PHStat check for the presence of multicollinerarity for all three independent variables. If it is present take necessary steps to eliminate it.

g. Construct a regression model using a general stepwise regression using the independent variables from part f. Using ?=0.05 for the p-vale to enter and remove independent variable.

h. Predict the average demand for an in-season week in which Bob has 65 prints in inventory priced at $59 per print using the model developed in part g.

i. Construct a 95% confidence interval for the regression coefficients for the price variable from part g. Be sure to interpret the meaning of this confidence interval.

j. Preform a residual analysis to verify that the conditions for the regression model are met for the model developed in part g.

X39 X V fx D E F G H A B C Sold Price ($) Inventory Season 1 2 9 62 64 In-season 3 7 62 52 In-season Off-season 4 6 49 43 60 In-season 9 55 5 62 53 Off-season 6 62 65 In-season UT DO U 8 55 66 In-season 10 49 64 In-season 55 53 In-season 49 61 In-season 59 62 In-season 52 64 In-season 69 64 Off-season 55 59 Off-season 59 67 In-season 49 77 In-season 65 56 Off-season 59 57 In-season 69 72 In-season 59 58 Off-season 59 57 In-season 49 55 In-season 65 57 Off-season 49 65 Off-season 52 60 In-season 62 47 Off-season 69 60 Off-season 55 63 In-season 55 65 In-season 59 65 Off-season 32 49 88 In-season 33 65 69 Off-season 34 62 71 Off-season 69 57 In-season 55 56 Off-season 49 67 In-season 38 65 59 Off-season 55 80 In-season 40 49 55 In-season 41 69 54 In-season 42 49 57 Off-season 43 62 73 In-season 44 49 47 Off-season 45 55 50 In-season 46 52 60 In-season Data Sheet2 Sheet3 Ready Lo 9 Type here to search