Department of Statistics and Actuarial Science

217 Schaeffer Hall

The University of Iowa

Iowa City, IA 52242

Phone: (319) 335-0818

Email: dale-zimmerman@uiowa.edu

- Spatial statistics, linear models, experimental design, multivariate
analysis, environmetrics, sports statistics

January 21: Sections 2.1 and 2.2

January 24: Sections 2.3 and 2.6

January 26: Sections 2.4 and 2.7

January 28: Sections 2.8 and 2.9

Jamuary 31: Sections 3.1 and 3.2

February 2: Section 3.3

February 4: Section 3.5

February 7: Section 3.6

February 9: Sections 3.4 and 4.1

February 11: Section 4.2

February 14: Section 4.3

February 16: Sections 5.1 and 5.2

February 18: Sections 5.3 and 5.4

February 21: Catch-up

February 23: First Midterm Exam

February 25: Sections 6.1-6.3

February 28: Section 6.4

March 2: Section 6.6-6.7

March 4: Section 7.2

March 7: Section 7.3

March 9: Sections 7.5-7.6

March 11: Section 7.10

March 21: Sections 8.1-8.2

March 23: Sections 8.3-8.4

March 25: Section 8.5

March 28: Sections 11.1-11.2

March 30: Sections 11.3-11.4

April 1: Section 11.5

April 4: Catch-up

April 6: Second Midterm Exam

April 8: Sections 9.1-9.2

April 11: Section 10.7

April 13: Section 9.4

April 15: Sections 2.5, 10.1-10.2

April 18: Sections 10.3, 10.5

April 20: Section 10.8

April 22: Section 10.9

April 25: Sections 12.1-12.2

April 27: No reading

April 29: No reading

May 2: No reading

May 4: No reading

May 6: No reading

Homework 2: Exercises 2.S.20, 2.S.22, 3.2.2, 3.2.3, 3.3.3, 3.3.4; Due Friday, February 4

Homework 3: Exercises 3.4.2, 3.4.4, 3.5.1, 3.5.7, 3.5.8, 3.6.1, 3.6.4, 3.6.7; Due Friday, February 11

Homework 4: Exercises 4.3.9, 4.3.11, 4.3.14, 4.3.15, 4.3.16, 5.2.5, 5.2.8, 5.2.9; Due Friday, February 18

Homework 5: Exercises 6.2.2, 6.3.4, 6.3.6, 6.3.7, 6.4.1, 6.4.5, 6.6.9, 6.7.7, 6.7.11; Due Friday, March 4

Homework 6: Exercises 7.2.5 (alternative hypothesis is two-sided), 7.2.9 (alternative hypothesis is two-sided), 7.2.12 (alternative hypothesis is two-sided), 7.2.13 (alternative hypothesis is two-sided), 7.3.10, 7.5.8 (alternative hypothesis is one-sided), 7.6.3; Due Friday, March 11

Homework 7: Exercises 7.10.4, 7.10.7, 8.2.2, 8.2.7, 8.2.9, 8.4.5, 8.4.9; Due Friday, March 25

Homework 8: Exercises 8.5.2, 8.5.5, 8.5.7, 11.2.2, 11.2.5, 11.4.1, 11.4.5, 11.4.6; Due Friday, April 1

Homework 9: Exercises 9.2.6, 9.2.9, 9.2.10, 9.4.4, 9.4.12, 9.S.14, 10.7.1, 10.7.6; Due Friday, April 15

Homework 10: Exercises 10.2.1, 10.2.6, 10.3.3, 10.3.8, 10.5.1, 10.5.4, 10.8.2, 10.8.4; Due Friday, April 22

Homework 11: Exercises 10.9.9, 10.S.9, 12.2.4, 12.2.10; Due Friday, April 29

Homework 12: Problem 1: For the data in Exercise 12.2.7 (i.e., X = laetisaric acid concentration, Y = fungus growth), (a) create the scatter plot; (b) obtain the least squares estimate of the intercept using the fact that the least squares estimate of the slope is -0.712; (c) plot the least squares line on your scatterplot; (d) predict the fungus growth when the laetisiric acid concentration is 15 (micrograms/ml); (e) obtain a 95% confidence interval for the slope using the fact that the estimated slope's standard error is 0.03589; (f) obtain a 95% confidience interval for the predicted fungus growth when the laetisiric acid concentration is 15 using the fact that this quantity's standard error is 1.8325. Problem 2: For the data in Exercise 12.3.8 (i.e., X = length, Y = maximum jump), (a) create the scatter plot; (b) obtain the least squares estimate of the intercept using the fact that the least squares estimate of the slope is 0.3492; (c) plot the least squares line on your scatterplot; (d) predict the maximum jump when the length is 150 mm; (e) perform a hypothesis test that the slope is equal to 0 at the 0.05 level of significance (against a two-sided alternative hypothesis) using the fact that the estimated slope's standard error is 0.3965; (f) obtain a 95% confidence interval for the predicted maximum jump when the length is 150 mm using the fact that this quantity's standard error is 18.96. Due Friday, May 6

January 21: Sections 2.1-2.4

January 24: Sections 2.5, 3.1-3.2

January 26: Sections 3.3-3.6

January 28: Sections 3.7-3.10

Jamuary 31: Sections 3.11, 4.1-4.2

February 2: Sections 4.3-4.5

February 4: Sections 5.1-5.2

February 7: Sections 5.3-5.4.3

February 9: Sections 5.4.4-5.4.9

February 11: Sections 5.5-5.9

February 14: Sections 6.1-6.3

February 16: Section 6.4

February 18: Sections 6.5-6.7, 7.1-7.2

February 21: Sections 7.3-7.4 (but skip stuff on power curves)

February 23: Sections 7.5-7.7, Supplement called "Optimal Allocation of EU's to Treatments"

February 25: Sections 8.1-8.3

February 28: Sections 8.4-8.6

March 2: Exam 1 (Coverage: Chapters 1-7)

March 4: Sections 8.7-8.9

March 7: Sections 8.10-8.11

March 9: Sections 9.1, 9.2.1-9.2.2

March 11: Sections 9.2.3-9.3

March 21: Sections 10.1-10.2

March 23: Sections 10.3-10.4.1

March 25: Sections 10.4.2-10.5

March 28: Sections 11.1-11.3

March 30: Sections 11.4-11.5

April 1: Sections 11.7-11.8

April 4: Sections 12.1-12.3

April 6: Section 12.4-12.5

April 8: Section 12.6

April 11: Exam 2 (Coverage: Chapters 8-11)

April 13: Section 12.7-12.8

April 15: Sections 13.1-13.2

April 18: Sections 13.3.1-13.3.4 plus supplement called "blocking-demonstration.pdf" in ICON course directory

April 20: Sections 13.4-13.5

April 22: Sections 14.0-14.1.1

April 25: Section 14.2

April 27: Sections 15.0-15.1.2

April 29: Sections 15.1.3-15.1.4

May 2: Sections 16.1-16.2

May 4: Sections 16.3-16.4

May 6:

Homework 2: Exercises 3.2 and 3.4, Problems 3.2 and 3.3; due February 2

Homework 3: Exercise 4.1, Problems 4.1 and 4.2, Question 4.1; due February 9

Homework 4: Exercise 5.1 (obtain Bonferroni, Scheffe, and Tukey-based simultaneous confidence intervals), Exercise 5.3 (in addition to the requested procedure that controls the SFER at epsilon=0.05, also perform procedures that control the FDR and EER at epsilon=0.05), Exercise 5.5 (use only the two-sided Dunnett procedure at epsilon=0.05), Problem 5.1 (use not only Tukey's HSD procedure but also the REGWR and SNK procedures at epsilon=0.05); due February 16

Homework 5: Exercises 6.1, 6.3 (be sure to check assumptions in addition to making the stated determinations), 6.5, 7.3, 7.5, Problem 7.1; due February 25

Homework 6: Exercise 8.1, Problems 8.1, 8.5, 8.6, 8.7; due March 11

Homework 7: Problems 9.1, 9.4, 9.5, Question 9.3. For the three problems, construct interaction plots and check for one-cell interaction, and, if appropriate, do Tukey's 1-df-for-nonadditivity test. Also for Problem 9.5, test for the significance of polynomial cisplatin dosage contrasts/terms, if warranted, after transforming the dosages to 0,1,...,5; due March 23

Homework 8: Exercises 10.2 and 10.4, Problems 10.2, 10.6, 10.8; due March 30

Homework 9: Exercises 11.3 (instead of a CI, obtain point estimates of variance components and determine whether there is evidence for type to type variability), 11.4, Problems 11.2 and 11.3 (only the point estimates, no CI); due April 6

Homework 10: Exercises 12.2 and 12.4, Problems 12.3, 12.4, 12.7; due April 18

Homework 11: Exercises 13.3 (don't transform), 13.4, 14.2, 14.4 (don't transform); Problems 13.3, 13.8 (don't transform), 13.11 (transform); due April 27, but can submit for full credit on April 29

Homework 12: Exercises 15.2, 15.3, Problems 15.1abc, 15.4, 16.2, 16.3; due May 6

2. Page 199: A better title for Section 5.5 would be "Distributions Associated with Sampling from Normal Distributions" 3. Exercise 6.4-21: The upper limit of support for x should be plus infinity, not theta.

4. Exercise 7.6-10: Symbols a, b, and c should be beta1-hat, beta2-hat, and beta3-hat, respectively.

5. Exercise 7.6-11: This exercise is correct, but out of place. Part (a) asks for the (sample) correlation coefficient, which is not defined until Section 9.6.

6. Exercise 7.7-6: This exercise is also correct but out of place as it involves calculation of the sample correlation coefficient, which is not defined until Section 9.6.

STAT:7290 Sports Statistics

R functions for antedependence models (under construction)

Data for antedependence models