Linear Regression

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This gives rise to the following output: The output indicates that the least-square regression line is given by. log( salary ˆ ) = −261.28 + 0. 13year The fraction of the variability in log(salary) that is explained by the least squares line of log(salary) on year is equal to 0.9622. Next, we want to calculate the predicted values from our regression. We can do this by typing: predict yhat, xb Note that other than creating a new variable, yhat, there will be no additional output. To plot the regression line together with the data type: scatter logsalary year || line yhat year The command above tells STATA to create a scatterplot of logsalary against year and to superimpose the line given by yhat. This command gives the following output logsalary/<strong>Linear</strong> prediction 0 1 2 3 1980 1985 1990 1995 2000 year logsalary <strong>Linear</strong> prediction
The line appears to fit the data well. However, we always want to make sure to check the residual plots. We can calculate the residuals by typing the command: predict r, resid Again, note that other than creating a new variable named r there will be no additional output. We can use this new variable to create a residual plot by typing: scatter r year This gives rise to the following output: Residuals -.4 -.2 -5.55e-17 .2 .4 1980 1985 1990 1995 2000 year The residual plot shows no apparent pattern. Homework: Do problems RII.8 and 10.9 from the textbook. Solve both of these problems using STATA. For each questions make sure to hand in (a) your log file, (b) a scatter plot with a regression line superimposed, (c) a residual plot, and (d) answers to all the questions in the text.
Page 1 and 2: Linear Regression In this tutorial
Page 3 and 4: The command above tells STATA to cr
Page 5: This gives rise to the following pl

Linear Regression

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