Question
Exercise on Multiple Regression Analysis of Detergent Demand Follow these instructions to complete this multiple regression assignment to forecast the sales (= demand) of detergent
Exercise on Multiple Regression Analysis of Detergent Demand
Follow these instructions to complete this multiple regression assignment to forecast the sales (= demand) of detergent cases.
1. Using the 30-week time series data in the table below, create a new Excel data file and do regression analysis in Excel on the data consisting of dependent variable Q and independent variables P, Px, A, and I.
Q = Detergent demand in cases; P = Price per case; Px = Competitor price; A = Advertising expenses; I = Household income.
Week | Q | P | Px | A | I |
1 | 1,290 | 137 | 94 | 814 | 53,123 |
2 | 1,177 | 147 | 81 | 896 | 51,749 |
3 | 1,155 | 149 | 89 | 852 | 49,881 |
4 | 1,299 | 117 | 92 | 854 | 43,589 |
5 | 1,166 | 135 | 86 | 810 | 42,799 |
6 | 1,186 | 143 | 79 | 768 | 55,565 |
7 | 1,293 | 113 | 91 | 978 | 37,959 |
8 | 1,322 | 111 | 82 | 821 | 47,196 |
9 | 1,338 | 109 | 81 | 843 | 50,163 |
10 | 1,160 | 129 | 82 | 849 | 39,080 |
11 | 1,293 | 124 | 91 | 797 | 43,263 |
12 | 1,413 | 117 | 76 | 988 | 51,291 |
13 | 1,299 | 106 | 90 | 914 | 38,343 |
14 | 1,238 | 135 | 88 | 913 | 39,473 |
15 | 1,467 | 117 | 99 | 867 | 51,501 |
16 | 1,089 | 147 | 76 | 785 | 37,809 |
17 | 1,203 | 124 | 83 | 817 | 41,471 |
18 | 1,474 | 103 | 98 | 846 | 46,663 |
19 | 1,235 | 140 | 78 | 768 | 55,839 |
20 | 1,367 | 115 | 83 | 856 | 47,438 |
21 | 1,310 | 119 | 76 | 771 | 54,348 |
22 | 1,331 | 138 | 100 | 947 | 45,066 |
23 | 1,293 | 122 | 90 | 831 | 44,166 |
24 | 1,437 | 105 | 86 | 905 | 55,380 |
25 | 1,165 | 145 | 96 | 996 | 38,656 |
26 | 1,328 | 138 | 97 | 929 | 46,084 |
27 | 1,515 | 116 | 97 | 1,000 | 52,249 |
28 | 1,223 | 148 | 84 | 951 | 50,855 |
29 | 1,293 | 134 | 88 | 848 | 54,546 |
30 | 1,215 | 127 | 87 | 891 | 38,085 |
5. Characterize (explain) the overall explanatory power of this multiple regression model in light of the R squared. (You will need to do an online search to learn about the meaning and importance of "R" to explain the model).
6. Applying your final model in forecasting: Use your regression model to forecast weekly detergent demand (Q) in the company's five new markets (A through E) based on the following expected data for the independent variables:
Regional independent variable forecast for the following period (week 31) | P | Px | A | I | FORECAST OF WEEKLY DEMAND OF DETERGENT (Q) |
Market A | 115 | 90 | 790 | 41,234 | ? |
Market B | 122 | 101 | 812 | 39,845 | ? |
Market C | 116 | 87 | 905 | 47,543 | ? |
Market D | 140 | 82 | 778 | 53,560 | ? |
Market E | 133 | 79 | 996 | 39,870 | ? |
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