04-13-2011 Linear Regression

04-13-2011 Linear Regression - 4/13/2011 Lecture 22: April...

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•4/13/2011 •1 Lecture 22: April 13, 2011 Chapter 12: – Last class I covered through page 501 – Today: page 501-middle page 505 – Next class: middle page 505-page 515 Homework No. 12: – Regression basics from last class and one topic covered today (called R 2 ). – It covers through page 503. – Opened today at 4:00 p.m. – Closes Friday, April 15 at 9:00 a.m. Connect Quiz 3: Chapter 12 – I will probably open this on Monday, April 18. -- I will probably close it on Friday, April 22 at 9:00 a.m. -- It will consist of correlation and regression topics: pages 488-515 -- A warm-up for the final exam Discussion Session 12: Friday, April 15 First item of business: Return to text problem 12.3 on page 493. (You calculated the correlation coefficient (r) for these data on April 8 during discussion.) Do all of the same column calculations again. Use formula (12.1) or formula (12.3) on page 490 to calculate r again. What is its interpretation for these two variables? Second item of business: Go to text problem 12.17 on page 503. This problem makes use of the same data as problem 12.3. Now, however, you will be computing the regression intercept and slope . Third item of business: The reason that I am having you reinvent the “large” table of calculations in Item (1) above is because the regression intercept and slope make use of the same table. So, calculate the intercept and slope by hand . Fourth item of business: Pay particular attention to the titles of the columns that you are computing. You are calculating various sums of squares or cross products . Their general notation is “SS” with appropriate subscripts. Fifth item of business: You will be making use of this “SS” information to create an ANOVA table for your regression model. (Here’s the link: your independent variable is a factor ; its various values are treatment s). The slope coefficient is a measurement of the treatment effect. Sixth item of business: You will calculate the ratio of the sum of squares due to the regression model (SSR) to the total sum of squares (SST). This is a measure of goodness of fit of the model.
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04-13-2011 Linear Regression - 4/13/2011 Lecture 22: April...

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