MATH 317: MATHEMATICAL STATISTICS

Aaron Robertson219 McGregory Hall

arobertson@colgate.edu

Meeting Place: On Tuesdays, we will be in the computer classroom (McGregory 210); on Thursdays,we will be in McGregory 226.

O�ce Hours: TR 9.00am to 10.00am and by appointment.

Book: Modern Mathematical Statistics with Applications, Second Edition by Jay Devore and KennethBerk.

Coverage: See the next two pages

Computer: We will be using Excel since this is widely available (and is good to know). We willhave one dedicated class period to getting familiar with Excel after which we will frequently use itduring Tuesday classes.

Assignments: Homework will be assigned and collected ten times throughout the semester. I willrandomly choose 5 problems to grade. Each problem is worth 5 points. There are also 3 lab reportsdue, each worth 25 points. The assignments are on the fourth page. If you lose this syllabus, youcan download it at www.aaronrobertson.org (so you might want to write this address down in yournotebook).

Testing: There will be 2 midterms and a final (that acts as a third midterm (i.e., only over contentafter the second midterm). The midterms are during the regular class time on Tuesday, February 17,and Tuesday, March 31.

The final is: Tuesday, May 5, at 3.00pm.

Late work: Late homework will not be accepted. Period. Homework is late once I leave theclassroom on the day homework is collected. Make-up midterms will not be given without a note fromyour doctor or dean. If you know you will miss a test, you must notify me before the test so that wecan make arrangements for you to take the test before the scheduled date.

Evaluation: The midterms and final are each worth 25%, and the total of all assignments is worth25%.

CLASS SCHEDULE

Date Section(s) Description Assignment for Next ClassJan. 19 (Mon) – Short class; review sheet handout Read Chapter 1

Review handoutJan. 20 (Tue) – Short review of handout HW #1

§3.4, §4.2 Moment generating functions– Chebyshev’s Inequality

Jan. 22 (Thu) – Using Normal Distribution§4.6 Probability plots§5.2 Covariance, correlation§5.5 Order statistics

Jan. 27 (Tue) – Intro to Excel HW #2Jan. 29 (Thu) §6.1 Distributions

§6.2 Central Limit TheoremFeb. 3 (Tue) §6.3 Linear Combinations of rvs

§6.4 Distribution related to Normal HW #3Feb. 5 (Thu) §7.1, §7.2 Point estimationFeb. 10 (Tue) §7.1 Bootstrapping and associated labFeb. 12 (Thu) §7.4 Fisher information HW #4

Review for Midterm 1 Study for Midterm 1Feb. 17 (Tue) – MIDTERM 1Feb. 19 (Thu) §8.1 – §8.4 Confidence intervalsFeb. 24 (Tue) §8.5 Bootstrapping and associated labFeb. 26 (Thu) §9.1 Hypothesis testing, errors

§9.4 p-values HW #5Mar. 3 (Tue) §9.2 Testing population mean

§9.3 Testing populatin proportionMar. 5 (Thu) §9.5 Power, likelihood ratio HW #6Mar. 10 (Tue) §10.1 Two sample Z test for means

§10.2 Two sample t test for meansMar. 12 (Thu) §10.3 Paired data tests

§10.4 Two sample Z test for proportionsMar. 17 (Tue) – No class: Spring BreakMar. 19 (Thu) – No class: Spring BreakMar. 24 (Tue) §10.5 F test Review worksheetMar. 26 (Thu) – Review for Midterm 2 HW #7

Study for Midterm 2Mar. 31 (Tue) – MIDTERM 2

(Continued on next page)

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CLASS SCHEDULE CONTINUED

Date Section(s) Description Assignment for Next ClassApr. 2 (Thu) §11.1 Single factor ANOVA

§11.2 Multiple comparisons in ANOVAApr. 7 (Tue) – ANOVA lab HW #8Apr. 9 (Thu) §12.1 Linear and logistic regression

§12.2 Estimating parametersApr. 14 (Tue) §12.3 Inferences about �1

§12.5 CorrelationApr. 16 (Thu) §12.6, §12.7 Residual plots & Multiple regressionApr. 21 (Tue) – Regression lab HW #9Apr. 23 (Thu) §13.1, §13.2 �2 testsApr. 28 (Tue) §13.3 Contingency tables HW #10Apr. 30 (Thu) §14.1 Wilcoxon Signed-Rank testMay 5 (Tue) – FINAL 3.00pm to 5.00pm

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HOMEWORK PROBLEMS

HW # Page Problem numbers Comments Due DateHW #1 – – Worksheet on mgfs, Jan. 22 (Thu)

Chebyshev InequalityHW #2 p. 219 98–100, 102, 104 Jan. 29 (Thu)

p. 252 24, 26, 29, 31p. 278 65, 66, 69, 73

HW #3 p. 295 1–3, 4b Crucial typo on p. 315: Feb. 5 (Thu)p. 304 11, 12, 14–17, 21–25 in the pdf of �2

p. 312 27–29, 35, 39, 42, 44, 45 the 21/2 should bep. 325 46b, 47, 48, 50a, 50b, 51, 52b, 63 2⌫/2

HW #4 p. 346 1, 3, 4, 6, 8, 9, 13, 16 Feb. 17 (Tue)p. 359 21, 23, 26, 27, 31p. 378 42, 43, 45a, 45b, 46a–c

HW #5 p. 390 3, 4, 10, 11 A bootstrapping lab Mar. 3 (Tue)p. 399 12, 14, 16, 17, 24, 25 report is also duep. 407 31, 34–36, 39p. 411 45–47

HW #6 p. 434 2–4, 6, 10 Mar. 10 (Tue)p. 447 17, 18, 25, 26, 30, 32, 33p. 454 36–38p. 465 48, 50, 55, 58p. 479 62, 63, 69–71

HW #7 p. 495 2, 3, 6, 8, 12 Mar. 31 (Tue)p. 505 21, 24, 27, 29, 30b, 36p. 517 40, 42p. 525 49, 50, 52, 56, 59p. 531 60, 64, 65

HW #8 p. 563 1–4, 10 An ANOVA lab Apr. 9 (Thu)p. 399 11, 12, 18, 21a report is also due

HW #9 p. 622 3, 7, 8, 11, 12b–d A regression lab Apr. 23 (Thu)p. 637 16–18, 21, 22 report is also duep. 651 31, 34–36p. 672 58–60, 63, 64p. 681 71, 72p. 699 78–81

HW #10 p. 730 3, 4, 6, 9 Apr. 30 (Thu)p. 742 13–15, 18, 22p. 752 24, 27–29p. 764 3, 4

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