Final answer:
The coefficient of determination, represented by R², measures the proportion of the dependent variable (Sales) that can be explained by the independent variable (Ads) in the regression model. The correct option is B.
Step-by-step explanation:
False. The statement is not correct. The coefficient of determination, represented by R², measures the proportion of the dependent variable (Sales) that can be explained by the independent variable (Ads) in the regression model. In this case, R² = 0.36, which means that approximately 36% of the variation in Sales can be explained by the variation in Ads. However, this does not imply that Ads explains 36% of the variation in Sales. It only measures the proportion of variation that can be explained by the model.
When we have an R squared (R²) value of .36 in a regression model, such as the one for Sales = 268 + 7.37 Ads, it means that 36 percent of the variation in Sales is indeed explained by the variation in Ads through the model. This interpretation of the coefficient of determination (R²) is correct, thus making the statement true. The R squared value reflects the proportion of the variance for the dependent variable (Sales) that's explained by the independent variable (Ads) in the model.
For example, if we have a different model with a correlation coefficient (r) of .6631, the resulting R squared value would be .6631² = .4397, indicating that approximately 44 percent of the variation in the dependent variable can be explained by the independent variable in that model.