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Question
NARRBEGIN: SA_71_74A carpet company, which sells and installs carpet, believes that there should be a relationship between the number of carpet installations that they will have to perform in a given month and the number of building permits that have been issued within the county where they are located. Below you will find a regression model that compares the relationship between the number of monthly carpet installations (Y) and the number of building permits that have been issued in a given month (X). The data represents monthly values for the past 10 months.
| Summary measures | |
| Multiple R | 0.5682 |
| R-Square | 0.3229 |
| StErr of Estimate | 9603.23 |
| ANOVA table | |||||
| Source | df | SS | MS | F | p-value |
| Explained | 1 | 351824479 | 351824479 | 3.8150 | 0.0866 |
| Unexplained | 8 | 737775521 | 92221940 |
| Regression coefficients | ||||
| Coefficient | Std Err | t-value | p-value | |
| Constant | -115076.69 | 82933.46 | -1.3876 | 0.2027 |
| Permits | 53.469 | 27.375 | 1.9532 | 0.0866 |
(A) Estimate the regression model. How well does this model fit the given data?
(B) Yes, there is a linear relationship between the number of carpet installations and the number of building permits issued at a = 0.10; The p-value = 0.0866 for the F-statistic. You can conclude that there is a significant linear relationship between these two variables.
(C) The Durbin-Watson statistic for this data was 1.2183. Given this information what would you conclude about the data?
(D) Given your answer in (C), would you recommend modifying the original regression model? If so, how would you modify it?
Answer
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