The estimated gross revenue for next week is $94,000. We are 95% confident that it will be between $93,500 and $94,440.
The estimated gross revenue for next week is $93,997. The 95% prediction interval is (93,500, 94,440). This means that we are 95% confident that the actual gross revenue will be between $93,500 and $94,440.
The estimated regression equation is 83.23 + 2.29x1 + 1.30x2, where x1 is the amount spent on television advertising and x2 is the amount spent on newspaper advertising. Both television and newspaper advertising have a positive effect on gross revenue.
This means that for every $1,000 increase in television advertising, we expect gross revenue to increase by $2,291. For every $1,000 increase in newspaper advertising, we expect gross revenue to increase by $1,302.
The R-squared value is 0.827, which means that 82.7% of the variation in gross revenue can be explained by the variation in television and newspaper advertising. The adjusted R-squared value is 0.784, which takes into account the number of independent variables in the model.
Overall, the multiple regression model seems to be a good fit for the data. The estimated coefficients are statistically significant and the R-squared values are relatively high. This suggests that the model can be used to make reliable predictions about gross revenue based on television and newspaper advertising expenditures.