Lecture12

Lecture12 - Announcements ! Information ! Our final exam is...

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Announcements ! Our final exam is Wednesday December 10 from 7-10pm (room assignments will be announced when I know). ! If you have a time conflict with another exam (i.e. Calculus), you must bring the proper form to me personally and I will add you to my list. ! The last day to contact me personally about a make-up exam is Monday December 1 st after class. (Tuesday, Dec. 2 for the TTh sections.) ! The time of the make-up exam is Thursday December 11 from 11-2pm. Unless you have a schedule conflict, you must take the exam during it’s regularly scheduled time. If this time does not work for someone with a conflict, you must arrange a make-up with your other class. ! During the week of November 24-28, there are no classes, discussions, stat labs, or quizzes. All activities will resume Monday 12/1. Information ! We can partition the total sum of squares into two sources of variation. ! If we are looking at the deviation between y i and y-bar, it can be split into two parts. Question ! How do the y i values vary around y-bar? ! Some of the difference is due to the difference between y- hat i and y-bar. " This difference is accounted for by the difference between x i and x-bar. ! The rest of the difference is due to the difference between y i and y-hat i " This difference is unexplained by the variation in x. " Represents variables not otherwise represented by the model Visualizing Errors in the Simple Model
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Understanding Variation ! Looking at the past equation as sums of squares… We see Variation in y = SSE + SSR ! SSE measures the amount of variation in y that remains unexplained ! SSR (SSM) measures the amount of variation in y that is explained by the variation in the independent variable x. Coefficient of determination, r 2 The coefficient of determination, r 2 , square of the correlation coefficient, is the percentage of the variance in y (vertical scatter from the regression line) that can be explained by changes in x . r 2 = variation in y caused by x (i.e., the regression line) total variation in observed y values around the mean Procedure 1) Develop a model theoretically and set a response and explanatory variable. 2) Collect data for the two variables (try to conduct an experiment). 3) Draw a scatter plot to see if a linear model is appropriate (also consider correlation) 4) Determine regression equation. 5) Calculate residuals and examine the residuals plot to determine if we have constant variance of the error term. 6)
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Lecture12 - Announcements ! Information ! Our final exam is...

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