Measurement and Statistics_Dasch_Date__040710

# Measurement and Statistics_Dasch_Date__040710 - Regression...

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Regression - regression is a way of predicting the value from one variable from another hypothetical model of the relationship between 2 variables linear model equation of a straight line Describing a straight line Y i = b 0 + b 1 X i + E i b 0 - the intercept (value of Y when X = 0) point at which the regression line crosses the Y-axis b i – regression coefficient for the predictor gradient or slope of the regression line direction/strength of relationship Intercepts and Gradients - gradients are also known as slopes - can have same intercepts and different slopes - can have same slopes and different gradients FIGURE 7.2 The Method of Least Squared - find the best fitting line with the smallest amount of error - want the least squared error

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FIGURE 7.3 Testing The Model SS T = total variance in the data SS M = improvement due to the model SS R = error in the model - if the model results in better prediction than using the mean, then we expect SS
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## This note was uploaded on 08/30/2010 for the course PSYC 209 taught by Professor Hoffman during the Spring '08 term at University of Delaware.

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Measurement and Statistics_Dasch_Date__040710 - Regression...

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