In the image you uploaded, the table shows the fitted values and residuals from a multiple regression performed on the cars dataset. I'll break down the two questions from the image:

1. Second regression fitted value:

   • According to the output under print(model.fittedvalues), the second fitted value corresponds to the index 1, which is 2.661578.

2. Fifth regression residual:

   • According to the output under print(model.resid), the fifth residual corresponds to the index 4, which is -0.784897.

Would you like any additional details or clarifications?

5 Related Questions:

1. What is the interpretation of residuals in regression analysis?
2. How do we calculate fitted values in a multiple regression model?
3. Why is minimizing the residuals important in regression?
4. What are predictor variables in a multiple regression model?
5. How does the inclusion of multiple predictors improve a regression model?

Tip:

In regression, residuals represent the difference between observed and predicted values. Ideally, these should be small and randomly distributed, indicating a good model fit.