In: Math
Write down a brief report of the results from this regression analysis explaining: (1) what is the impact of each variable over the demand? (2) How strong are the results from this analysis to support a forecast? (3) What are the limitations you foresee by using this analysis to forecast production for the following five years?
SUMMARY OUTPUT | |||||||||
Regression Statistics | |||||||||
Multiple R | 0.72916937 | ||||||||
R Square | 0.53168797 | ||||||||
Adjusted R Square | 0.51496254 | ||||||||
Standard Error | 72.98925047 | ||||||||
Observations | 30 | ||||||||
ANOVA | |||||||||
df | SS | MS | F | Significance F | |||||
Regression | 1 | 169354.741 | 169354.741 | 31.7891965 | 4.866E-06 | ||||
Residual | 28 | 149168.059 | 5327.43068 | ||||||
Total | 29 | 318522.8 | |||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | ||
Intercept | 1963.58187 | 120.949007 | 16.2347911 | 8.8864E-16 | 1715.82906 | 2211.33468 | 1715.82906 | 2211.33468 | |
Price per Case, P | -5.336865119 | 0.9465563 | -5.6381909 | 4.866E-06 | -7.2757978 | -3.3979324 | -7.2757978 | -3.3979324 | |
Q=1963.58-5.34*P | |||||||||
ep = | -0.527356143 | ||||||||