PSY 202 Chapter 7 - Day 2
PSY 202 Chapter 7 - Day 2 Psy 202
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This 1 page Class Notes was uploaded by Stephanie on Sunday October 2, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Matthew Mervin in Fall 2016. Since its upload, it has received 8 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.
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Date Created: 10/02/16
PSY 202: Elementary Statistics Chapter 7: Describing the Relationship between 2 Quantitative Variables: Regression – Day 2 I. Proportion of Variance explained a. This the gauge of how well X predicts Y i. The higher the proportion of variation is the better X predicts Y ii. No shared variance 1. No relationship between X and Y 2. 0 correlation iii. Small shared variance 1. As X changes Y changes but it does not tell a lot iv. Large shared variance v. We want the shared area to be bid because it tells how much X can predict Y vi. Whatever is left of Y is the residual variance 1. We want a large explained variance and a small residual variance vii. Calculation 1. Take the correlation coefficient and then square it b. Reversing the roles of the Independent Variable and the Dependent Variable i. If you switch X and Y the slope will differ but they are connected ii. The geometric mean equals the correlation coefficient II. The Standardized Regression Solution a. Use the zscore for X to predict the zscore for Y i. Because the mean is 0 the yintercept will always equal 0 ii. The slope equals the correlation coefficient III. The Use of Regression for Prediction a. The predicted value of Y for X is always the sample mean for Y b. Most of the errors are at the edges of the regression line than in the middle i. There is no variability in the middle c. The standard error of the prediction(s) i. Confidence Interval 1. Predict the average value of Y at every X ii. Prediction Interval 1. Predict the value of Y for a single case at a particular X a. Predict individual values d. Cook’s distance i. If a point is larger than 1 then it has a substantial influence ii. It can tell that the regression line moves but not by how much
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