Psyc_315_-_Winter_2010_-_Class_9

Psyc_315_-_Winter_2010_-_Class_9 - Recap of Last Class...

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1 Recap of Last Class • Steps to Calculating Pearson r • Correlation and Causation • Factors Influencing Pearson r • Prediction • Questions? Chapter 8 Correlation and Prediction 3 Recap: Correlation vs. Prediction • When a bivariate trend is reasonably linear, a line of “best fit” can be found and used to predict values of Y from X. • Such a line is called a regression line , and the prediction is made by noting the Y value of the point on the line that corresponds to the particular value of X. • For r = ±1.00, each predicted Y value would fall exactly on the regression line, and prediction would be errorless. • With any lesser correlation, there will be some prediction error . The lower the correlation, the greater the prediction error.
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4 Recap: Correlations and Predictions • Correlations can be used to make predictions about scores. Predictor: – X variable – Variable being predicted from (e.g., SAT scores). – Independent variable Criterion: – Y variable. – Variable being predicted (e.g., First year GPA). – Dependent variable 5 The Regression Equation • Every straight line has an equation; the regression line is no exception. • The location of the regression line in a scatterplot is determined by the regression equation . • A straight line is defined by the slope and the intercept . • The slope (b) reflects the angle and direction of the regression line. By how many units Y increases or decreases as X increases by 1 unit. • The
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This note was uploaded on 04/29/2010 for the course PSYCH 315 taught by Professor Afroditipanagopoulos during the Winter '10 term at Concordia Canada.

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Psyc_315_-_Winter_2010_-_Class_9 - Recap of Last Class...

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