Transcription of Lecture 2 Linear Regression: A Model for the Mean
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Lecture 2 Linear Regression: A Model for the Mean
Lecture 2 Linear Regression: A Model for the MeanSharyn O HalloranSpring 20052U9611 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)Spring 20053U9611 Linear regression : Introduction Data: (Yi, Xi) 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 termsSpring valuesPHEstimated regression lineSteer example (see Display , p. 177).73 Intercept= for estimated regression line:Equation for estimated regression line:^Fitted lineError termSpring 20055U9611 Create a new variableltime=log(time) regression analysisSpring 20056U9611 regression TerminologyRegressionRegression: the mean of a response variable as a function of one or more explanatory variables: {Y | X} regression modelRegression Model : an ideal formula to a
U9611 Spring 2005 2 Closer Look at: Linear Regression Model Least squares procedure Inferential tools Confidence and Prediction Intervals Assumptions …
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