In: Statistics and Probability
For the data below, the regression model Yi=−401.3488+1.9721X1i−0.0993X2i uses total staff present and remote hours to predict standby hours. Develop a regression model to predict standby hours that includes total staff present, remote hours, and the interaction of total staff present and remote hours.
| 
 Standby  | 
 Total Staff  | 
 Remote  | 
||||
|---|---|---|---|---|---|---|
| 
 287  | 
 312  | 
 381  | 
||||
| 
 271  | 
 358  | 
 656  | 
||||
| 
 188  | 
 325  | 
 336  | 
||||
| 
 188  | 
 322  | 
 267  | 
||||
| 
 197  | 
 317  | 
 235  | 
||||
| 
 261  | 
 315  | 
 164  | 
||||
| 
 118  | 
 293  | 
 399  | 
||||
| 
 116  | 
 325  | 
 343  | 
||||
| 
 147  | 
 311  | 
 338  | 
||||
| 
 177  | 
 333  | 
 598  | 
||||
A. At the 0.10 level of significance, is there evidence that the interaction term makes a significant contribution to the model?
B. Which regression model is more appropriate, the model with the interaction term used in (a) or the originalmodel?
A)
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.600807 | |||||
| R Square | 0.360969 | |||||
| Adjusted R Square | 0.041453 | |||||
| Standard Error | 59.72854 | |||||
| Observations | 10 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 3 | 12091.01 | 4030.336 | 1.129737 | 0.409191 | |
| Residual | 6 | 21404.99 | 3567.498 | |||
| Total | 9 | 33496 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 2133.193 | 2069.986 | 1.030535 | 0.342498 | -2931.88 | 7198.267 | 
| TOTAL STAFF | -5.86834 | 6.42947 | -0.91273 | 0.396579 | -21.6007 | 9.864004 | 
| REMOTE | -5.22811 | 4.094418 | -1.27689 | 0.24883 | -15.2468 | 4.790574 | 
| INTERACTION | 0.015645 | 0.012479 | 1.253656 | 0.256601 | -0.01489 | 0.046181 | 
FROM ABOVE TABLE ,
P VALUE FOR INTERACTION COEFFICIENT = 0.2566
P VALUE > 0.1 , NOT SIGNIFICANT
there IS NOT SUFFICIENT evidence that the interaction term makes a significant contribution to the model
................
B)
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.439977 | |||||
| R Square | 0.193579 | |||||
| Adjusted R Square | -0.03683 | |||||
| Standard Error | 62.11955 | |||||
| Observations | 10 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 2 | 6484.134 | 3242.067 | 0.840167 | 0.47094 | |
| Residual | 7 | 27011.87 | 3858.838 | |||
| Total | 9 | 33496 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | -401.349 | 462.1929 | -0.86836 | 0.413976 | -1494.26 | 691.5638 | 
| TOTAL STAFF | 1.972145 | 1.551138 | 1.271418 | 0.244193 | -1.69571 | 5.640003 | 
| REMOTE | -0.09929 | 0.171813 | -0.57791 | 0.581431 | -0.50556 | 0.30698 | 
MODAL WITH INTERACTION TERM IS MORE USEFUL AS MULTIPLE R AN R SQUARE ARE GOOD IN INTERACTION MODAL
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