= Let (Xi, Yi), i = 1,2, ..., n be a sample of n paired data, also let yi = bo + b2xi be the simple regression line of the data. The least squares estimate of by represents (a) predicted value of y when x = 0. (b) the expected change in y per unit change in x. (c) the predicted value of y. (d) variation around the line of regression.
A regression model was fitted and the residuals were checked to be approximated normally distributed. The scatter plot of residuals vs predicted values on the right consists of 104 observations. The RMSE: 100- 50- (a) is around 0. (b) is around 40 (c) is around 25. (d) cannot be estimated from the plot. -50- 100 120 140 160 180 200 220 240 260
The residual plot for a linear regression model is shown below. Which of the following is true? (a) A linear model is okay because the association between the two variables is fairly strong. (b) The linear model is not good because the correlation is near 0. (c) The linear model is not good because some residuals are large. (d) The linear model is not good because of the curvature in the residuals.
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