assumptions - Lecture 2 Linear Regression A Model for the...

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Lecture 2 Linear Regression: A Model for the Mean Sharyn O’Halloran
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Spring 2005 2 U9611 Closer Look at: Linear Regression Model Least squares procedure Inferential tools Confidence and Prediction Intervals Assumptions Robustness Model checking Log transformation (of Y , X , or both)
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Spring 2005 3 U9611 Linear Regression: Introduction Data: ( Y i , X i ) for i = 1,...,n Interest is in the probability distribution of Y as a function of X Linear Regression model: Mean of Y is a straight line function of X, plus an error term or residual Goal is to find the best fit line that minimizes the sum of the error terms
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Spring 2005 4 U9611 5.5 6 6.5 7 PH 0 1 2 ltime Fitted values PH Estimated regression line Steer example (see Display 7.3, p. 177) .73 Intercept=6.98 1 Y= 6.98 Y= 6.98 - .73X .73X Equation for estimated regression line: Equation for estimated regression line: ^ Fitted line Error term
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