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Chapter 7

# PSY201 Chapter 7 Regression

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University of Toronto St. George

Psychology

PSY201H1

Kristie Dukewich

Fall

Description

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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