1. Confidence Intervals, Cross-validation, and Predictor Selection
Prediction
Confidence Intervals, Cross-validation, and Predictor Selection

2. Skill Set
Why is the confidence interval for an individual point larger than for the regression line?
Describe the steps in forward (backward, stepwise, blockwise, all possible regressions) predictor selection.
What is cross-validation? Why is it important?
What are the main problems as far as R-square and prediction are concerned with forward (backward, stepwise, blockwise, all possible regressions)

3. Prediction v. Explanation
Prediction is important for practice
WWII pilot training
Ability tests, e.g., eye-hand coordination
Built an airplane that flew
Fear of heights
Favorite flavor ice cream
Age and driving accidents
Explanation is crucial for theory. Highly correlated vbls may not help predict, but may help explain. Team outcomes as function of team resources and team backup.

4. Confidence Intervals CI for the line, i.e., the mean score:
Note shape.
=MSR. N= sample size. The df are for MSR (variance of residuals).
CI for a single person’s score:

5. Computing Confidence Intervals
Suppose:
Find CI for line (mean) at X=1.
df = N-k-1 = = 18.
CI = 3.81 to 7.79
For an individual at X=1, what is the CI?
CI = .29 to 11.31

6. Review
Why is the confidence interval for the individual wider than a similar interval for the regression line?
Why are the confidence intervals regression curved instead of being straight lines?

7. Shrinkage
R2 is biased (sample value is too large) because of capitalizing on chance to minimize SSe in sample.
If the population value of R2 is zero, the expected value in the sample is R2 =k/(N-1) where k is the number of predictors and N is the number of people in the sample. If you have many predictors, you can make R2 as large as you want. What is the expected value of R-square if N = 101 and k =10? Ethical issue here.
Common adjustment or shrinkage formula:
Use summary(lm.object) to see R-square and adjusted R-square.

8. Shrinkage Examples N= Adj R2 15 .497 30 .583 100 .625 N= Adj R2 15
Suppose R2 is with k = 4 predictors and a sample size of 30. Then
R2 = .6405 N=
Adj R2 15
.497 30
.583
100
.625
R2 = .30 N=
Adj R2 15
.020 30
.188
100
.271
Note small N means lots of shrinkage but also smaller initial R2 shrinks more.

9. Cross-Validation
Compute a and b(s) (can have one or more IVs) on initial sample.
Find new sample, do not estimate a and b, but use a and b to find Y’.
Compute correlation between Y and Y’ in new sample; square. Ta da! Cross- validation R2.
Cross-validation R2 does not capitalize on chance and estimates operational R2.

10. Cross-validation (2) Double cross-validation Data splitting
Expert judgment weights (don’t try this at home)
Math Estimates
Fixed:
Random:

11. Review
What is shrinkage in the context of multiple regression? What are the things that affect the expected amount of shrinkage?
What is cross-validation? Why is it important?

12. Predictor Selection Widely misunderstood and widely misused.
Algorithms labeled forward, backward, stepwise, etc.
NEVER use for work involving theory or explanation (hint: this clearly means your thesis and dissertation).
NEVER use for estimating importance of variables.
Use SOLELY for economy (toss predictors).

13. All Possible Regressions
Data from Pedhazur example.
GPA (Y)
GREQ
GREV
MAT AR 1
.611
.581
.468
.604
.267
.426
.621
.508
.405
.525
Mean
3.313
67.00
3.567
S.D.
.600
48.618
83.03
9.248
.838
GPA is grade point average. GREQ is Graduate Record Exam, Quantitative. GREV is GRE Verbal. MAT is Miller Analogies Test. AR is Arithmetic Reasoning test.

14. All Possible Regressions (2)k R2
Variables in Model 1
.385 AR
.384
GREQ
.365
MAT
.338
GREV 2
.583
GREQ MAT
.515
GREV AR
.503
GREQ AR
.493
GREV MAT
.492
MAT AR
.485
GREQ GREV 3
.617
GREQ GREV MAT
.610
GREQ MAT AR
.572
GREV MAT AR
GREQ GREV AR 4
.640
GREQ GREV MAT AR
Note how easy it is to choose the model with the highest R2 for any given number of predictors. In predictor selection, you also need to worry about cost. You get both V and Q GRE in one test. Also consider what change in R2 means. Accuracy in prediction of dropout.

15. Predictor Selection Algorithms
Forward – build up from start with p value. End when no variables meet PIN. May include duds.
Backward – Start with all vbls and pull out with POUT. May lose gems.
Stepwise – Start forward, check backward at each step. Not guaranteed to give best R2.
Blockwise – not used much. Forward by blocks, then any method (eg stepwise) within block to choose best predictors.

16. Things to Consider in PS
Algorithms consider statistical significance, but you have to consider practical significance and cost, i.e., algorithms don’t work well.
Surviving variables are often there by chance. Do the analysis again and you would choose a different set. OK for prediction.
The value of correlated variables is quite different when considered in path analysis and SEM.

17. Hierarchical Regression
Alternative to predictor selection algorithms
Theory based (a priori) tests of increments to R-square

18. Example of Hierarchical Reg
Does personality increase prediction of med school success beyond that afforded by cognitive ability?
Collect data on 250 med students for first two years.
Model 1:
R2=.10 , p<.05
Model 2
R2=.13 , p<.05
Model test:
F(2,245)=4.22, p < .05

19. Review
Describe the steps in forward (backward, stepwise, blockwise, all possible regressions) predictor selection.
What are the main problems as far as R-square and prediction are concerned with forward (backward, stepwise, blockwise, all possible regressions)
Why avoid predictor selection algorithms when doing substantive research (when you want to explain variance in the DV)?