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The Role of r 2 in Regression Target Goal: I can use r 2 to explain the variation of y that is explained by the LSRL. D4: 3.2b Hw: pg 191 – 43, 46, 48, 53, 63,

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r : correlation coefficient r 2 : the coefficient of determination If the line ŷ is a poor model, the value of r 2 turns out to be: too small, closer to 0. If the line ŷ fit the data fairly well, the value of r 2 turns out to be: larger, closer to 1.

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What is the meaning of r 2 in regression? Squares of the deviations about ŷ

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Least-Squares Regression The Role of r 2 in Regression The standard deviation of the residuals gives us a numerical estimate of the average size of ourprediction errors. There is another numerical quantitythat tells us how well the least-squares regressionline predicts values of the response y. Definition: The coefficient of determination r 2 is the fraction of the variation in the values of y that is accounted for by the least-squares regression line of y on x. We can calculate r 2 using the following formula: where and

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Formula for r 2 made up of these parts SST: total sum of squares about the mean y bar. SST = ∑(y – y bar) 2 SSE: sum of the squares for error. SSE = ∑(y – ŷ) 2 r 2 : coefficient of determination. r 2 = SST – SSE SST

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Ex: Large r 2 If, then the deviations and thus the ; in fact, if all of the points fell exactly on the regression line, SSE would be 0. r 2 = SST – SSE SST x is a good predictor of y SSE would be small

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For the data in this example, x: 0510 y: 078 r 2 = SST – SSE = 38 – 6 = SST38 Conclusion: We say that ____ of the ___________ is explained by the __________________________ % variation in y least-squares regression of y on x.

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r 2 in Regression The coefficient of determination r 2, is the fraction of the variation in the values that are explained by least-squared regression of y on x.

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Least-Squares Regression The Role of r 2 in Regression r 2 tells us how much better the LSRL does at predicting values of y than simply guessing the mean y for each value in the dataset. Consider the example on page 179. If we needed to predict abackpack weight for a new hiker, but didn’t know each hikersweight, we could use the average backpack weight as ourprediction. If we use the mean backpack weight as our prediction, the sum of the squared residuals is SST = If we use the LSRL to make our predictions, the sum of the squared residuals is SSE = SSE/SST = 30.97/83.87 SSE/SST = Therefore, 36.8% of the variation in pack weight is unaccounted for by the least-squares regression line. SSE/SST = 30.97/83.87 SSE/SST = Therefore, 36.8% of the variation in pack weight is unaccounted for by the least-squares regression line. 1 – SSE/SST = 1 – 30.97/83.87 r 2 = % of the variation in backpack weight is accounted for by the linear model relating pack weight to body weight. 1 – SSE/SST = 1 – 30.97/83.87 r 2 = % of the variation in backpack weight is accounted for by the linear model relating pack weight to body weight.

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Least-Squares Regression Correlation and Regression Wisdom Correlation and regression are powerful tools for describing the relationship between twovariables. When you use these tools, be awareof their limitations Fact 1. The distinction between explanatory and response variables is important in regression.

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Facts about least-squared regression Fact 2: There is a close connection between the slope of the least-squared regression line. As the correlation grows less strong, the in response to changes in x. correlation and prediction ŷ moves less

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Fact 3: Every LSRL passes through Remember: When reporting a regression, give r 2 as a measure of how successful the regression was in explaining the response. When you see a correlation (r), square it to get a better feel for the strength of the association.

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Exercise: Predicting The Stock Market. Some people think that the behavior of the stock market in January predicts its behavior for the rest of the year. Take the explanatory variable x to be the percent change in a stock market index in January and the response variable y to be the change in the index for the entire year. We expect a positive correlation between x and y because the change during January contributes to the years full change.

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Calculation from the data for the years 1960 to 1997 gives: x bar = 1.75%, s x = 5.36 y-bar = 9.07%,s y = 15.35% and r = 0.596

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r = a.What percent of the observed variation in yearly changes in the index with is explained by a straight-line relationship the changes during January? The straight-line relationship is explained by r 2 = or, 35.5% of the variations in yearly changes in the index is explained by the changes during January.

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b.What is the equation of the least-squared regression line for predicting full-year change from January change? Find b:, b = Find a:, a = 6.083%

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The regression equation is ŷ = a + bx ŷ = 6.083% x

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Predictions c.The mean change in January is = 1.75%. Use your regression line to predict the change in the index in a year in which the index rises 1.75% (x bar) in January. Why could you have given this result w/out doing the calculation? Every LSRL passes through (x bar, y bar). Recall y bar = 9.07%, so the predicted change is ŷ = 9.07%.

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Exercise: Class attendance and grades A study of class attendance and grades among first year students at a state university showed that in general students who attended a higher percent of their classes earn higher grades.

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Class attendance explained 16% of the variation in grade index among students. What is the numerical value of the correlation between percent of class attended and grade index? r2r2 r = High attendance goes with high grades so the correlation must be positive. 0.40

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