ch. 6_092110 - Discussion section 227 09/21/10 Regression...

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Discussion section 227 09/21/10 Regression Regression is a statistical tool that allows us to predict scores of one variable based on the other variable. We are assuming that the relationship between the two variables can best be described by a straight line The line that passes through our data points with the minimum amount of error (distance between actual points and the line) is called line of best fit The regression line equation The equation describing a straight line is Y = a + b(x) where b is the slope and a is the Y intercept. We can use Z scores to compute this equation c Intercept: predicted value of Y when X is equal to zero c Slope: the amount that Y is predicted to increase for an increase of 1 in X Example 1: Student # of absences (X) Statistics test scores (Y) 1 2 85 2 1 90 3 4 78 4 0 95 5 3 80 M X = 2.0 M Y = 85.6 SD X = 1.58 SD Y = 7.02 r = -0.63
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Computing the regressio 1. Find the Y intercept – I. Convert raw sco II. Find Z(Y hat) III. Convert to raw s 0.80*7.02+85.6
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This note was uploaded on 02/21/2012 for the course PYSC 227 taught by Professor Fairchild during the Fall '10 term at South Carolina.

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ch. 6_092110 - Discussion section 227 09/21/10 Regression...

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