Business Forcasting Solutions

CASE 5-1:  THE SMALL ENGINE DOCTOR

1.

                   [pic 1]   

2.

         [pic 2]

3.                   SEASONAL                   FITTED VALUES AND

                  ADJUSTMENT                   FORECASTS, T*S

          MONTH         FACTORS           2005     2006      2007    

           Jan                 0.693                  8.68     17.32     25.97    

           Feb                      0.707                  9.59     18.41     27.23    

           Mar                  0.935                13.66     25.34     30.01    

           Apr                  1.142                17.87     32.13     46.38    

           May                 1.526                25.48     44.52     63.57    

           Jun                   1.940                34.39     58.61     82.82  

           Jul                    1.479                27.77     46.23     64.69  

           Aug                  0.998                19.77     32.23     44.68  

           Sep                   0.757                15.78     25.22     34.67  

           Oct                   0.373                  8.17     12.83     17.49  

           Nov                  0.291                  6.68     10.32     13.95    

           Dec                  1.290                30.94      47.06     63.17    

        [pic 3]

4.

                [pic 4]

          [pic 5]

             [pic 6]

1. Trend*Seasonality (T*S):        MAD = 1.52

      Linear Trend Model:                MAD = 9.87

1. If we had to limit our choices to the models in 2 and 4, the linear trend model is better than any of the Holt smoothing procedures. We judged by MAD and MSE. The Trend*Seasonality (T*S) model is best. This procedure is the only one that takes account of the trend and seasonality in Small Engine Doctor sales.  

CHAPTER 6

6.         A, B, and D

                      [pic 7]

            Regression equation : Books = 32.46 + 36.41 Feet    (Positive linear relationship)

    S = 17.9671   R-Sq = 90.3%   R-Sq(adj) = 89.2%

    Analysis of Variance

    Source            DF             SS           MS            F           P

    Regression         1    27032.3    27032.3     83.74    0.000

    Error                  9     2905.4         322.8

    Total                10    29937.6

C.  The correlation between Books and Feet is .950

E.  Reject [pic 8] at the 10% level since F = 83.74 and its p value = .000 < .10. Could also use t = 9.15 and its p value = .000. Since the slope coefficient is significantly different from 0, the correlation coefficient is significantly different from 0.