Regression problem using statistical relations with predictors and a dependent variable. Attachments in MS Word.

Part of a CFO's job is to determine the direction of the company and which divisions/projects should be expanded. This CFO selects a sample of 30 projects and wants to find out about the relationship between Profit (Y measured in 1000's) per project and number of Employee Hours (EmployHrs) involved in each project (measured in 100's), length of time to complete the project (Months) measured in months, whether the project was completed on time (Ontime=1 if on time and 0 otherwise), and then the type of construction project (Retail, Manufacturing, or Government). Retail =1 if it was a retail project, 0 otherwise. Manufacturing =1 if it was a manufacturing project and 0 otherwise, Government is omitted.

See the attached file for the regression and questions.

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Solution Preview help you form the regression equation:

(profit) = (1.1447) + (0.6323)(EmployHrs)- (0.003012)(Months)+ (0.18035)(Ontime)+ (0.01104)(Retail)+ (0.4429)(Manufact)

Now, since the P-value in the ANOVA table is less than 0.01, there is a statistically significant relationship between the variables at the 99% confidence level.
The R2 statistics indicates that the model as fitted explains about 71.1% of the variability observed in the dependent variable (the "profit" in your case). However the adjusted R2 statistics is more suitable for comparing models with different numbers of independent variables. In your case the value indicates that only 65.1% of the ...