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Question: Follow these instructions to complete this multiple regression assignment to forecast the sales (…



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 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 SUMMARY OUTPUT
1 1,290 137 94 814 53,123
2 1,177 147 81 896 51,749 Regression Statistics
3 1,155 149 89 852 49,881 Multiple R 0.460424
4 1,299 117 92 854 43,589 R Square 0.211991
5 1,166 135 86 810 42,799 Adjusted R Square 0.047822
6 1,186 143 79 768 55,565 Standard Error 8.590332
7 1,293 113 91 978 37,959 Observations 30
8 1,322 111 82 821 47,196
9 1,338 109 81 843 50,163 ANOVA
10 1,160 129 82 849 39,080 df SS MS F Significance F
11 1,293 124 91 797 43,263 Regression 5 476.4487 95.28975 1.291297 0.300426
12 1,413 117 76 988 51,291 Residual 24 1771.051 73.7938
13 1,299 106 90 914 38,343 Total 29 2247.5
14 1,238 135 88 913 39,473
15 1,467 117 99 867 51,501 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
16 1,089 147 76 785 37,809 Intercept -101.135 52.16986 -1.93858 0.0644 -208.809 6.53785 -208.809 6.53785
17 1,203 124 83 817 41,471 Q 0.085372 0.049127 1.737762 0.09507 -0.01602 0.186765 -0.01602 0.186765
18 1,474 103 98 846 46,663 P 0.428392 0.271586 1.577373 0.127801 -0.13213 0.988918 -0.13213 0.988918
19 1,235 140 78 768 55,839 Px -0.32566 0.34355 -0.94794 0.352609 -1.03472 0.383388 -1.03472 0.383388
20 1,367 115 83 856 47,438 A 0.010646 0.030297 0.351403 0.728353 -0.05188 0.073176 -0.05188 0.073176
21 1,310 119 76 771 54,348 I -0.00061 0.000504 -1.20172 0.241195 -0.00165 0.000435 -0.00165 0.000435
22 1,331 138 100 947 45,066
23 1,293 122 90 831 44,166    Y =
101.135+0.085372(Q+0.428392(P)-0.32566(PX)+0.010646(A)-0.00061(I)=
-100.93686
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
2. Show your
regression output.
3. Build and present
the regression model (long equation) to predict Q (dependent
variable).
4. Interpret the
coefficient estimate of each independent variable (i.e, when “A”
increases by one unit, what changes occur in “Q”?).
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 FORECASTED 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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