Follow these instructions to complete this multiple regression assignment to forecast the sales (= demand) of detergent cases. 

1. Using the 30week 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  Pvalue  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 â€œRâ€ to explain the model). 

6. Applying your final model in forecasting: Use your regression model to forecast weekly detergent demand (Q) in the companyâ€™s five 

new markets (A through E) based
