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Chapter 7

PSY201 Chapter 7 Regression

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University of Toronto St. George
Kristie Dukewich

Chapter 7 Regression - Regression considers using the relationship between two or more variables for prediction - Regression line is a best fitting line used for prediction - The least-squares regression line is the prediction line that minimizes the total error of prediction according to least-squares - The regression of Y on X - Minimize prediction errors in the Y variable - Maximize ability to predict Y given X - Regression constants are a_Y and b_Y - The regression of X on Y - Minimize prediction errors in the X variable - Maximize ability to predict X given Y - Regression constants are a_X and b_X - The standard error of estimate is a measure of the average deviation of the prediction errors about the regression line - To measure the magnitude of prediction errors - The amount of error involved in predicting a score from the regression line - The average amount of unexplainable variation in Y given X - We must assume that the variability of Y remains constant as we go from one X score to the next - The assumption of homoscedasticity - To measure the prediction accuracy - The larger the value, the less accurate the prediction - The smaller the value, the more accurate the prediction - 68% of the scores fall ±1 SE around the regression l
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