PSPP is amazing, and I would love to use it in my college classrooms. However, right now it lacks the features needed to actually conduct regression analyses responsibly by checking your assumptions. There are dozens of these, of course, but I think that three in particular would be very helpful to putting PSPP on the map for introductory statistics courses looking for a free GUI based teaching tool. I am not a programmer, but I feel like I should give suggestions on what would be most useful for PSPP in teaching a "second class" on regression
1) OLS Assumption #1: Negligible multicollinearity: The ability to estimate Variance Inflation Factors would give a tool for testing the presence of multicollinearity.
References: Thiel's Principles of Econometrics; basic computations can be found here: https://newonlinecourses.science.psu.edu/stat501/node/347/
2) Assumption 2: Outliers are handled and noninfluential The ability to identify outliers in multivariate models by calculating Cook's Distance (aka Cook's D statistics) would help with finding outliers.
Reference: Cook, R. Dennis (March 1979). "Influential Observations in Linear Regression". Journal of the American Statistical Association. American Statistical Association. 74 (365): 169–174.
3) Assumption 3: Linearity: Componentplusresidual plots can visually identify nonlinear associations in many cases.
Overview: https://www.stat.washington.edu/pds/stat423/Documents/LectureNotes/notes.423.ch12.pdf
4) Assumption 4: Heteroskedasticity: Residual vs fitted plots, which PSPP all but supports already since it can output residuals and predicted values. It would just be a matter of temporarily taking those values, standardizing them, and scatter plotting the two.
There are, of course, other assumptions and tests, but this is a good start. I am happy to test these features if they are implemented.
