Question

La Cabaña, a popular motel chain in the southwest, is interested in developing a regression model that can predict the occupancy rate % of its motels. Currently, the company is interested in using two explanatory variables to predict occupancy. They want to use the amount of advertising in $ used by each motel and if the particular location a franchised location. Some regression information is presented below:

Summary measures

Multiple R 0.5358
R-Square 0.2871
Adj R-Square 0.2223
StErr of Estimate 7.582

Regression coefficients

Coefficient Std Err t-value p-value

Constant 43.118 11.4263 3.7735 0.0010

Advertising 0.0013 0.0006 2.4119 0.0247

Franchise 3.038 3.1759 0.9567 0.3491

If we write the linear regression model as f$hat{Y}=a+bX_{1}+cX_{2}f$ , where is Advertising, and is Franchise, then from the above information, we can infer that a is _____________, b is _________________, and c is ____________________. (Please keep three decimal points.)

The coefficient of determination is 0.2871; this represents __________________ percentage of the variation in the occupancy can be explained by this regression equation. (Please keep two decimal points.)

From the p-values, we may conclude that at 5% confidence level, all the coefficients are statistically ______________________ after refining the regressors. (Please only fill in "significant" or "insignificant".)

Answer 1