Chapter_9.2

# Chapter_9.2 - Section 9.2 Linear Regression Larson/Farber...

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Section 9.2 Linear Regression 1 Larson/Farber 4th ed.

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Section 9.2 Objectives Find the equation of a regression line Predict y -values using a regression equation 2 Larson/Farber 4th ed.
Regression lines After verifying that the linear correlation between two variables is significant, next we determine the equation of the line that best models the data ( regression line ). Can be used to predict the value of y for a given value of x . x y 3 Larson/Farber 4th ed.

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Residuals Residual The difference between the observed y -value and the predicted y -value for a given x -value on the line. For a given x -value, d i = (observed y -value) – (predicted y -value) x y } d 1 } d 2 d 3 { d 4 { } d 5 d 6 { Predicted y -value Observed y -value 4 Larson/Farber 4th ed.
Regression line ( line of best fit ) The line for which the sum of the squares of the residuals is a minimum. The equation of a regression line for an independent variable x and a dependent variable y is ŷ = mx + b Regression Line Predicted y -value for a given x - value Slope y -intercept 5 Larson/Farber 4th ed.

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The Equation of a Regression Line ŷ = mx
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## This note was uploaded on 10/22/2011 for the course ACCT 3551 taught by Professor Brown during the Spring '11 term at UNC.

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Chapter_9.2 - Section 9.2 Linear Regression Larson/Farber...

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