I made a gui patch to graph a plot for residual vs fitted on regression analyze. For now it graph only PRED1 and RESID1. I looking for a resolve to graph PRED2,3... and RESID2,3... You can test it.Attached screenshot and patch.

file #46800)

John,

Thank you for getting the Collin package rolling. I have not been able to test yet because I can't figure out how to compile the files that are laying around, but I'll test things out once I can figure it out.

John,

Here's what I'm aware of. I teach basic SPSS, but I don't use it in my own work, so I apologize for my lack of knowledge on SPSS syntax.

1) VIFs are under SPSS's "Statistics" option in the regression GUI, listed as "collinearity diagnostics." It is called via the COLLIN option, such as in the fake data illustration below:

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT VAR00001
  /METHOD=ENTER VAR00002

2) Cook's D can be attained after a regression model in SPSS by adding /SAVE COOK as an option to the regression command. For instance:

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT VAR00001
  /METHOD=ENTER VAR00002
  /SAVE COOK.

3) In a program like Stata you can get this via the command "cprplot" after a regression was run. In SPSS it isn't as straightforward, but someone else might know better. I'd say this is the lowest priority, as a simple scatter plot between two variables will get us what we need, which is doable in current PSPP. It might be an extension of PSPP instead of a match of SPSS's features.

4) Residual vs fitted plots are implemented in SPSS via a plot option attached to the regression commands. This usually looks something like: SCATTERPLOT=(*ZPRED ,*ZRESID) in syntax. See below, for instance, for a full command:

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT VAR00001
  /METHOD=ENTER VAR00002
  /PARTIALPLOT ALL
  /SCATTERPLOT=(*ZPRED ,*ZRESID)

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 non-influential- 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: Component-plus-residual plots can visually identify non-linear 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.