--------------- help for regpt ---------------Regression with a Moving Point ------------------------------

. regpt, [ r(correlation) n(sample size) ]

When you start regpt, you can supply the correlation between X and Y and the sample size, or accept the default correlation of .3 and sample size of 30. Based on the correlation and sample size, points are randomly generated and then plotted showing a scatterplot with regression line. You are then shown a dialogue box that allows you to move a "Moving Point", shown as a yellow square (assuming you are using the default Stata color scheme).

You can move the point in the Y axis, adjusting its distance from the regression line without the point included, i.e. (Y-Yhat) . As you adjust the point, you can compare the regression line without the point (in green) with the regression line with the point (in red).

You can move the point in the X axis. As you move the point along the X axis, the distance from the regression line without the point (Y-Yhat) is held constant. This allows you to see the consequences of varying X while keeping Y-Yhat constant.

As you move the "Moving Point", you can see the equation for the regression line with the point change (and compare it to the regression line without the point). You can also see some Regression Diagnostic Statistics for the regression equation with the point in the equation, namely the residual, leverage and Cook's D.

By default, you are shown a plot that has a scatterplot and regression line. You can choose to view other plots - Scatterplot with Regression Line - Residual vs. Fitted Plot - Leverage vs. Residual Squared - Leverage vs. Residual Squared with Cook's D (Cook's D is indicated by the size of the circle). When you change the Plot type, you can click "Reshow Now" to immediately switch plot types.

When you are done, you can click the Done button. You can reset X to 0, and Y-Yhat to 0 via the Reset button. You can see this help window by clicking Help. Author ------

Statistical Consulting Group Institute for Digital Research and Education, UCLA idrestat@ucla.edu