Lecture12

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

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Announcements ! Our final exam is Thursday May 7 from 7-10pm (room assignments will be announced prior to the final via email and the class web site). ! 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 Tuesday April 21. If you miss this deadline, you must take the exam during its scheduled time. ! The time of the make-up exam is Friday May 8 from 10 - 1pm. 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 April 27- May 1, there are no classes, discussions, stat labs, or quizzes. Review sessions begin Monday May 4. 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)
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Lecture12 - Announcements ! Information ! Our final exam is...

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