Question

If a residual plot has [description missing] and the R-squared value is [value missing], then it is a good model for making predictions about the response variable.

a) Homoscedasticity; 0.95
b) Heteroscedasticity; 0.20
c) Linearity; 0.75
d) Multicollinearity; 0.50

Answer 1

The presence of linearity in the residual plot and a high R-squared value indicate a good model for making predictions.

Step-by-step explanation:

The given question is asking about the criteria for a good model for making predictions about the response variable based on the features of the residual plot and the R-squared value. To determine if a model is good for predictions, we need to consider two factors:

1. The presence of linearity in the residual plot.
2. The value of the R-squared (coefficient of determination).

If a residual plot exhibits homoscedasticity and the R-squared value is relatively high, then it can be considered a good model for making predictions about the response variable. Therefore, based on the given options, the correct answer would be:

b) Heteroscedasticity; 0.20