Linear Regression

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This command gives rise to the following output in the results window: The output indicates that the least-square regression line is given by. fat ˆ = −62.55 + 2. 22waist This implies that for each additional inch in waist size, the model predicts an increase of 2.22% body fat. The fraction of the variability in fat that is explained by the least squares line of fat on waist is equal to 0.7865. Next, we want to calculate the predicted values from the regression. We can do this by typing: predict yhat, xb This command is solely used to create a new variable, yhat, and there will be no output in the results window. However, if you look in the variables window a new variable yhat is now present. To plot the regression line together with the data type: scatter fat waist || line yhat waist A vertical line can be obtained by simultaneously pressing the shift and the backslash (\) button on your keyboard. This button is located directly above the enter key. To obtain two vertical lines, repeat this procedure twice.
The command above tells STATA to create a scatterplot of fat against waist and superimpose the line given by yhat created in the previous command. This command gives the following plot: fat/<strong>Linear</strong> prediction 0 10 20 30 40 30 35 40 45 waist fat <strong>Linear</strong> prediction The line appears to fit the data well. However, it is important to make residual plots when performing regression. We can calculate the residuals by typing the command: predict r, resid Again, note that other than creating a new variable, r, there will be no additional output. The new variable consists of the set of residuals, and a residual plot can be created by typing: scatter r waist This gives rise to the following plot: Residuals -10 -5 0 5 10 30 35 40 45 waist The residual plot shows no apparent pattern. The residual plot and the relatively 2 high value of R indicate that the linear model we fit is appropriate.
Page 1: Linear Regression In this tutorial
Page 5 and 6: This gives rise to the following pl
Page 7: The line appears to fit the data we

Linear Regression

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