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4-27-2011 Homoscedasticity

# 4-27-2011 Homoscedasticity - Lecture 25 Discussion this...

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•4/27/2011 •1 Lecture 25: April 27, 2011 Discussion this Friday: Overall significance of a regression equation Prediction interval for an individual Y Identifying heteroscedasticity and autocorrelation Multiple regression Final Exam: Thursday, May 5: 8:00 a.m., Boyden Gym Topics for the final exam: Everything I covered after Exam 2 I’ll have detailed pages on Monday. Review sessions (Tentative until rooms are confirmed): Friday, April 29, 3:30 p.m., Stockbridge 124 Wednesday, May 4, 3:30 p.m., Thompson 104 Last Connect activities Homework No. 13 and Quiz No. 5 Each will be ready Thursday evening and due next Wednesday. Note: Here are the kinds of questions you will be asked in Homework No. 13 and Quiz No. 5. You will be given a familiar section of the regression output and asked any of the following: – to fill in a blank – for an interpretation of a number – to do a calculation To do this successfully, you must: – be familiar with all vocabulary items – know how a vocabulary item is calculated We will be doing all of this today. I am finished with Chapter 12. Let’s move to Chapter 13. Multiple regression. It is an extension of bivariate regression.

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•4/27/2011 •2 The simple regression model from the previous three classes I will put a “1” subscript on the “X” variable. Reason: There is only one independent variable in this simple model. This is called a bivariate regression model. – “bi” “variate” two variables Y and X 1 Is there any reason to have only one independent variable? The model could be or 0 1 Y= X+ 0 1 1 Y= X 0 1 1 2 2 Y= X + X 0 1 1 2 2 3 3 Y= X + X X A more general regression model If there are “k” variables, in general, the model is: This is Equation (13.1) on page 545 of the text. This is no longer a bivariate regression model. Since there are multiple variables, this is called a multiple regression model. It has “k” predictor variables or independent variables (in addition to an intercept). So, the model has “k+1” parameters. Let’s look closely at the components of this model. 0 1 1 2 2 k k Y= X + X ... X Model: Y is the response variable or dependent variable. X 1 , X 2 , …, X k are independent variables or predictor variables or covariates.
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4-27-2011 Homoscedasticity - Lecture 25 Discussion this...

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