Back to M/STAT 502 syllabus…

STAT 502 aims to cover Chapters 5–9 in Casella and Berger, continuing from STAT 501.

Week 1

Mon Jan 11

  • Introductions to the course
  • Section 5.1: Basic concepts of random samples
  • Section 5.2: Sums of random variables from a random sample

Wed Jan 13

  • Section 5.3: Sampling from the normal distribution

Fri Jan 15

  • Section 5.3 (cont): Sampling from the normal distribution

Week 2

Mon Jan 18

Martin Luther King Day: No class

Wed Jan 20

  • Section 5.4: Order statistics
  • Homework 1 due (Sections 5.1-1.3) in Gradescope by 5:00pm

Fri Jan 22

  • Section 5.4 (cont): Order statistics

Week 3

Mon Jan 25

  • Section 5.5: Convergence concepts

Wed Jan 27

  • No class
  • Video: Convergence in Distribution
  • Video: Central Limit Theorem
  • QUIZ 1 in Gradescope 11am–1pm (45 min time limit): Sections 5.1-5.3

Fri Jan 29

  • Section 5.5 (cont): Convergence concepts
  • (Skip Section 5.6)

Week 4

Mon Feb 1

  • Section 7.1-7.2: Methods of finding estimators - method of moments

Wed Feb 3

  • Section 6.3.1: The likelihood function
  • Section 7.2 (cont): Methods of finding estimators - maximum likelihood

Fri Feb 5

  • Section 7.2 (cont): Methods of finding estimators - maximum likelihood
  • Homework 2 due (Sections 5.4-5.5) in Gradescope by 5:00pm

Week 5

Mon Feb 8

  • Section 7.2 (cont): Methods of finding estimators - Bayes

Wed Feb 10

  • No class
  • Video: Derivation of Beta-Binomial Bayes Estimator
  • Video: Beta-Binomial Bayes Estimator Example
  • Video: Conjugate Families
  • QUIZ 2 in Gradescope 11am–1pm (45 min time limit): Sections 5.4-5.5

Fri Feb 12

  • Section 7.3: Methods of evaluating estimators

Week 6

Mon Feb 15

President’s Day: No class

Wed Feb 17

  • Section 7.3 (cont): Methods of evaluating estimators

Fri Feb 19

  • Sections 6.1-6.2: Sufficiency principle
  • Homework 3 due (Sections 7.1-7.2) in Gradescope by 5:00pm

Week 7

Mon Feb 22

  • Sections 6.2 (cont), Section 7.3.3: Sufficiency principle

Wed Feb 24

  • No class
  • Video: Ancillary Statistics
  • Video: Complete Statistics
  • Video: Example: Complete Sufficient Statistic
  • QUIZ 3 in Gradescope 11am–6pm (45 min time limit): Sections 7.1-7.2

Fri Feb 26

  • Sections 6.2 (cont), Section 7.3.3: Sufficiency principle

Week 8

Mon Mar 1

  • Sections 6.2 (cont), Section 7.3.3: Sufficiency principle

Wed Mar 3

  • Section 10.1: Asymptotic evaluations — point estimation

Fri Mar 5

  • Section 7.3.4: Loss function optimality
  • Homework 4 due (Sections 7.3.1-7.3.4, 6.2, 10.1) in Gradescope by 5:00pm

Week 9

Mon Mar 8

  • Simulation example: Consistency
  • Section 8.1: Introduction to hypothesis testing
  • Project proposal due in Gradescope by 5:00pm

Wed Mar 10

  • No class
  • QUIZ 4 in Gradescope 11am–1pm (45 min time limit): Sections 7.3.1-7.3.4, 6.2, 10.1

Fri Mar 12

  • Section 8.3.1: Methods for evaluating tests

Week 10

Mon Mar 15

  • Section 8.3.1 (cont): Methods for evaluating tests

Wed Mar 17

  • Section 8.3.1 (cont): Methods for evaluating tests

Fri Mar 19

  • Section 8.2.1: Likelihood ratio tests
  • Homework 5 due (Sections 8.1 and 8.3.1) in Gradescope by 5:00pm

Week 11

Mon Mar 22

  • Section 8.2.1 (cont): Likelihood ratio tests

Wed Mar 24

  • No class
  • QUIZ 5 in Gradescope 11am–1pm (45 min time limit): Sections 8.1 and 8.3.1

Fri Mar 26

  • Section 8.3.2: Uniformly most powerful tests

Week 12

Mon Mar 29

  • Section 8.3.2 (cont) and 8.3.4: Uniformly most powerful tests and p-values

Wed Mar 31

  • Section 9.1: Introduction to interval estimation

Fri Apr 2

  • University Day: No class
  • Homework 6 due (Sections 8.2.1, 8.3.2, and 8.3.4) in Gradescope by 5:00pm

Week 13

Mon Apr 5

  • Section 9.2: Methods of finding interval estimators
  • Draft project report due in Gradescope by 5:00pm

Wed Apr 7

  • No class
  • QUIZ 6 in Gradescope 11am–1pm (45 min time limit): Sections 8.2.1, 8.3.2, and 8.3.4

Fri Apr 9

  • Section 9.2 (cont): Methods of finding interval estimators

Week 14

Mon Apr 12

  • Section 9.3: Methods of evaluating interval estimators

Wed Apr 14

  • Mathematical statistics applied to linear models

Fri Apr 16

  • Mathematical statistics applied to linear models
  • Final project report due in D2L by 5:00pm

Week 15

Mon Apr 19

  • Mathematical statistics applied to linear models

Tue Apr 20

  • Project presentation slides due in D2L (pdf version) by 11:59pm

Wed Apr 21

  • Project presentations
  • Homework 7 due (Sections 9.1-9.3) in Gradescope by 5:00pm

Fri Apr 23

  • Project presentations
  • Optional QUIZ 7 in Gradescope 8am–5pm (45 min time limit): Sections 9.1-9.3
    • Quizzes 1–6 comprise your quiz grade for this course, where the lowest grade from Quizzes 1–6 will be dropped. If your score on Quiz 7 is higher than the second lowest score from Quizzes 1–6 (i.e., the lowest quiz score that counts towards your grade), then your score on that quiz will be replaced by your score on this quiz. If you do not take Quiz 7, or if your score on Quiz 7 is lower than the second lowest score from Quizzes 1–6, then your grade will not change.

Week 16

Finals week: Take-home final exam released in D2L and Gradescope at 8:00am on Monday, April 26; due in Gradescope by 5:00pm on Thursday, April 29.