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Unformatted text preview: Chapter 8 Linear Regression Model We can model the relationship between 2 quantitative variables with a line and give its equation. The equation will let us predict a response (y) value given an explanatory (x) value. Clearly no line can go through all the points (unless the correlation is 1 or 1). The linear model is just an equation of a straight line through the data. The points in the scatterplot dont all line up, but a straight line can summarize the general pattern. Residual We call the estimate made from a model the predicted value, and write it as (called yhat). The difference between the observed value and its associated predicted value is called the residual. ^ y (cont.) The residual value tells us how far off the models prediction is at that point. To find the residuals, we always subtract the predicted value from the observed one. ^ Residual y y = Line of Best Fit The line of best fit is the line for which the sum of the squared residuals is smallest....
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This note was uploaded on 09/16/2011 for the course AMAT 108 taught by Professor Sutphin during the Fall '07 term at SUNY Albany.
 Fall '07
 sutphin

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