Spring 20052U9611Closer Look at:Linear Regression ModelLeast squares procedureInferential toolsConfidence and Prediction IntervalsAssumptionsRobustness Model checkingLog transformation (of Y, X, or both)
Spring 20053U9611Linear Regression: IntroductionData: (Yi, Xi) for i = 1,...,nInterest is in the probability distribution of Y as a function of XLinear Regression model: Mean of Y is a straight line function of X, plus an error term or residualGoal is to find the best fit line that minimizes the sum of the error terms
has intentionally blurred sections.
Sign up to view the full version.
Spring 20054U96115.566.57PH012ltimeFitted valuesPHEstimated regression lineSteer example (see Display 7.3, p. 177).73Intercept=6.981Y= 6.98Y= 6.98-.73X.73XEquation for estimated regression line:Equation for estimated regression line:^Fitted lineError term