Spring Semester, 2000
Other materials (including the syllabus and assignments) are available
class web page.
- I will have a review session for questions related to the
final exam next Monday evening (May 8), from
8:00 pm -- 9:30 pm in SH-DH 351 (not room 215 as previously
- I will have office hours Tuesday, May 9, from 3-5 and on
Wednesday from 2-3:30.
- I am missing scores for some of you for one or two assignments.
To make sure there is no error in the recording of grades,
please let me know if you have the missing assignment. If
you didn't seem to do any assignments, I omitted your name
from the list. I have updated the list to include PS #5 ,
which quite a few of you evidently did not do.
If there's an error in this list, simply bring me the marked
assignment, and I will fix the grades.
| Name1 || ps1, ps3 |
| Name2 || ps2, ps5 |
- Some review notes on regression are
here . Its 10 pages long, so
I do not have copies to distribute. Have a look, and print it
if you want a paper copy. Evidently, the printed version is a lot
more easy to read than when you view the document with Acrobat.
- The final exam is on Wednesday, May 10
The make-up final exam is scheduled for Friday,
September 8, 2000, from 4-6 pm. If you do not take the final
exam on May 10, you will be required to take the
make-up final next fall.
- The average for the second exam was 85 with an SD of 14.
The median was 91 and mode was 92.
- The mean for the first midterm was about 80 with SD = 13.
You can figure out your relative standing from these summaries since
the grades are close to normal - albeit a bit skewed.
- I have the impression that some of you are not aware of the
grading distribution for the material in this class.
I have given them when asked, but not published it here. It
was in the class syllabus. Here it is:
- Homework 10%
- Project 10%
- First test 25%
- Second test 25%
- Final 30%
- For questions related to the project please look at
the list of "frequently asked questions"
- For JMP questions, you should always see the TAs at the
Stat Lab for help. They are there most of the day M-F and
can show you how to use JMP for doing the calculations.
Their schedule is on the
class web page.
For the datasets from the regression casebook, go to this
Extra handouts, including some JMP files.
- Class survey data
Analysis of survey data
- Power curve JMP file
- ROC curve JMP file
- Paired testing handout (computer retail advertising)
Software Ads jmp file
- Summary of one and two-sample tests
Claimed color percentages for M&Ms (Does not load on some systems!)
- M&Ms JMP data file
- Example of chi-square data file
Illustrates the use of JMP-IN to compute chi-square values from tables.
- Mon (March 6): One-way analysis of variance
- Wed (March 8): Using one-way anova
(Mileage comparison JMP data )
- Mon (March 20): Two-way anova with some review of one-way
(Web page experiment JMP data )
- Wed (March 22): Using two-way anova with
a sample of test flight data
- Mon (March 27): Introduction to regression
- Wed (March 29):
Introduction to inference in regression
- Mon (April 3):
Review for second midterm
- Wed (April 5): Beta and regression
This example introduces the notion of 'beta' as used in finance.
Interpret the results of the calculations with a bit of common sense,
as we have not taken into account the cost of borrowing or transactions.
- Mon (April 10):
Prediction and outliers in regression
- Wed (April 12):
Introduction to multiple regression
A reading for this class covers an
automobile design case .
- Mon (April 17):
Multiple regression and collinearity.
- Wed (April 19):
Categorical variables in regression.
- Mon (April 24): Categorical variables, continued
- Wed (April 26): Summary regression modeling
- Mon (May 8): Review
- Review of methods
- Confidence intervals
- Comparisons of two groups (t-test)
- Comparisons of several groups (anova)
- Anova summary and F-test
- Multiple comparisons (Tukey, Hsu)
- R2, anova table, and F-ratio
- Regression coefficients, SE, and t-ratio
- Marginal vs partial coefficients
- Collinearity (VIF)
- Categorical predictors, interaction
- Prediction, RMSE, and prediction intervals
- Assumptions and residual diagnostics
- Leverage points and outliers
- Sample data analyses
- Questions on prior exams