Spring 2000 Syllabus for Statistics 112

• Jan 18th: Introduction. Read (Reread?) 2.1 (skip p. 115), 2.2 and 2.3 (skip pp. 143-146). Look at problems 2.5, 2.36 and 2.38.
• Jan 20th: Correlation and LS. Read section 2.4 and 2.5. Look at problems 2.10, 2.19 (don't compute, look at your graph and estimate the answer), 2.49 (again, do it graphically) Bring your graph and answer to class on Tuesday.

• Jan 25th: SNOW DAY!
• Jan 27th: Residuals, JMP IN. Read 2.7.

• Feb 1st: Regression model and parameters. Lurking variables. Read carefully 662 - 673.
• Feb 3rd: Assumptions. Finish reading section 10.1. (pages 673 - 681)

  Sample problems 2.20 (don't compute 2.20(b)--just guess), 2.25, 2.29, 2.34, 2.35.

  Either by yourself or with a partner do the following two JMP problems. Try to work on them this weekend so if you have any problems you can bring questions to class on tuesday. Turn them in on Thursday Feb 10th.

  1. 2.58 (jmp data)( ascii data).
  2. 2.65 jmp data)( (ascii data)
     • (a) Hint: To color points: First make a historgram of left-vs-right and then use the Row's command to color left-vs-right. Also do Rows-symbols to make one x and the other o.
     • (c) Hint: To do a regression for each hand seperately use the "rows exclude" command. You might also do a "rows hide" to make your graphs more understandable.
     • (d) Hint: Use the fit model command then use the dollar to save the residuals. Finally plot the residuals vs. the row number.

• Feb 8th: predictions and Hypothesis testing. Read carefully 673 -- 679.
• Feb 10th: Finishing up simple regression. Some homeworks to try are:
  • 10.6 (type the numbers into JMP).
  • For these three questions, 10.8, 10.9, 10.10, again type the data into JMP. To make a prediction for 1918 add the year to the X column and leave the Y column blank. Use the cross-hairs to get precise values for the prediction intervals. Try using a quadradic fit for the prediction interval also. Does this seem to have better coverage?
  • 2.76 (ascii data) or type it in yourself. Transform the Y using logs and do a fit. Make a prediction interval. Now actually take the log of the population and do a linear fit and prediction interval. Use your calculator to convert this prediction interval into one that predicts population. (by raising the e to the log-population.) It should agree with the first prediction interval.
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• Feb 15th: Introduction to multiple regression Read 712 - 715.
• Feb 17th: More on Car89 (Read handout)

• Feb 22th: Partial vs marginal slope Read 719 - 732.
• Feb 24th: Collecting data

• Feb 29th: Analysing class data and summary regression.
• Mar 1st: Evening review with Mwape at Vance Hall 210, 6 - 8 p.m.
• Mar 2nd: Midterm 1 (Exam covers: chapter 2 (not 2.6), chapter 10 (not pp 684-685), chapter 11 (not pp 717-718). Primary handouts not in the book: Ideal regression/problem regressions, Tukey bulging rule, Car89.

  Makeup exam Tuesday March 7, 4:30 - 6:00. Check excuse with Dean ahead of time.

• Mar 7th: Discrimination over the years (Homework 2 due)
• Mar 9th: Modeling categorical data (Project teams due) (email answers to nuria@wharton.upenn.edu and dean@foster.net)

• March 14th: SPRING BREAK!
• March 16th: SPRING BREAK!

• March 21st: Plots using categories
• March 23rd: Last discrimination example Read pages 199 - 211 of bulk pack. (optional due date for project description :-) As announced in project.

• March 28th: non-parallel lines (Project description due)
• March 30th: multiple testing (Read chapter 12)

• April 4th: ANOVA and contrasts (Read chapter 12)
• April 6th: Model building data collection day (bring your surveys and stay after to collect data)

• April 11th: Two way ANOVA (Look over 13)
• April 13th: (1) Getting started with your data and (2) Proving causation

• April 18th: Experiments using two-way ANOVA (read chapter 13)
• April 20th: So your Y is categorical? (Project data analysis due--turn in xerox if possible)

• April 25th: Dealing with non-independence.
• April 27th: Review (Write up of project due)