Let's evaluate our model's results. Generate a scatter plot of the residuals against the fitted values allowing us to visually inspect whether the residuals have constant variance and are distributed randomly around the zero residual line.What does the scatter plot tell us?OptionsThe model has perfect predictive accuracy.The plot indicates homoscedasticity as residuals have a constant variance and are distributed randomly around the zero residual line.There is no linear relationship between the dependent and independent variables.The model suffers from multicollinearity.

Plot the residuals against the fitted values.

What does a linear regression model being heteroscedastic imply?The variance in the data is constantThe variance in the data is not constantThe variance in the data is zero

Which assumption of linear regression is violated if the residuals exhibit a pattern when plotted against the predicted values?

17.Question 17What does a residual plot help you determine?1 pointIt’s a visual way to see if there is a pattern in your data set.It’s a visual way to see if your coefficients of determination are accurate.It’s a visual way to see if your model needs more input variables.It’s a visual way to see if a linear plot is appropriate.

Assumptions of Simple Linear RegressionWhat will be the effect of the error terms not being homoscedastic in nature?You will not be able to fit a straight line through the data.The error terms will not be normally distributed. The inferences made on the model would be unreliable.Both B and C.