Chapter 6 (p153) Predicting Future Performance Criterion-Related Validation – Kind of relation between X and Y (regression) – Degree of relation (validity coefficient) Strength? Significant? How accurate are predictions? Regression & Correlation – What’s the difference between the two? Significance Testing Chapter 6 Predicting Future Performance1

VALIDATION AS HYPOTHESIS TESTING BIVARIATE REGRESSION – Linear Functions MEASURES OF CORRELATION – Basic Concepts in Correlation Residual and Error of Estimate Generalized Definition of Correlation Coefficient of Determination Third Variables Null Hypothesis and its Rejection Chapter 6 Predicting Future Performance2

– The Product-Moment Coefficient of Correlation – What are these? Explain each Non-linearity Homoscedasticity and Equality of Prediction Error Correlated Error Unreliability Reduced Variance Group Heterogeneity Questionable Data Points A summary Caveat – Don’t over-estimate what you have – Sometimes you can’t control everything – You may need to get more data – Work with what you have Chapter 6 Predicting Future Performance3

– Statistical Significance The Logic of Significance Testing – Under what conditions could a low validity coefficient of.20 be useful? Type I and Type II Errors and Statistical Power – Which is more important I or II? – How can you control power? – What are the three things power is affected by? » Explain why for each Chapter 6 Predicting Future Performance4

COMMENT ON STATISTICAL PREDICTION – What is the standard error of estimate? – Why is it an important consideration for prediction? – What is a problem with restriction range restriction in The predictor The criterion – What could be done about it? – Give an example of a curvilinear relationship between a predictor and creiterion Chapter 6 Predicting Future Performance5