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Unformatted text preview: R 2 SST = 2 1 ) ( ∑ =n i y y = total sum of squares for y (the response variable) SSR = 2 1 ) ( ∑ = ∧n i y y = sum of squares for the regression SSE = 2 1 ) ( ∑ = ∧n i y y = sum of the errors squared It can be mathematically shown that SST = SSR + SSE Define r 2 = SST SSR 2) Residual (error) Analysis a) Is the linear model reasonable b) Are the errors: i) normally distributed about the regression line ii) of constant variance for all x values 3) Influential observations  Influential points  points which when removed from the data will result in a significant change in the slope and/or intercept of the regression line Outliers  points outside the general pattern of data...
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This note was uploaded on 12/05/2011 for the course MATH 2040 taught by Professor Raysievers during the Fall '10 term at Utah Valley University.
 Fall '10
 RaySievers
 Statistics, Correlation, Scatter Plots

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