Calendar

Students are required to attend class sessions and studio sessions, the latter of which take place on Friday. Typically, studio sessions are used to introduce longer problems and the use of R.

WEEK # SES # TOPICS READINGS SLIDES PROBLEM SETS
Probability
1 C1 Introduction, counting and sets

1a: Introduction (PDF)

1b: Counting and Sets (PDF)

Reading Questions for 1b

Reading Questions for R Intro

Class 1 Slides (PDF)

Class 1 Slides with Solutions (PDF)

Problem Set 1 (PDF)

Problem Set 1 Checker

Problem Set 1 Solutions (PDF)
C2 Probability basics

2: Probability: Terminology and Examples (PDF)

Reading Questions for 2

R Tutorial 1A: Basics

R Tutorial 1B: Random Numbers

Class 2 Slides (PDF)

Class 2 Slides with Solutions (PDF)
S1 Birthday matches; Introduction to R    
2 C3 Conditional probability, Bayes' theorem

3: Conditional Probability, Independence and Bayes' Theorem (PDF)

Reading Questions for 3

Class 3 Slides (PDF)

Class 3 Slides with Solutions (PDF)

Problem Set 2 (PDF)

Problem Set 2 Checker

Problem Set 2 Solutions (PDF)
C4 Discrete random variables, expectation

Reading Questions for R

4a: Discrete Random Variables (PDF)

Reading Questions for 4a

4b: Discrete Random Variables: Expected Value (PDF)

Reading Questions for 4b

Class 4 Slides (PDF)

Class 4 Slides with Solutions (PDF)
S2 Expectation; Simulation using R    
3 C5 Variance, continuous random variables

5a: Variance of Discrete Random Variables (PDF)

Reading Questions for 5a

5b: Continuous Random Variables (PDF)

Reading Questions for 5b

Class 5 Slides (PDF)

Class 5 Slides with Solutions (PDF)

Problem Set 3 (PDF)

Problem Set 3 Checker

Problem Set 3 Solutions (PDF)
S3 Gallery of continuous variables, histograms

5c: Gallery of Continuous Random Variables (PDF)

Reading Questions for 5c

5d: Manipulating Continuous Random Variables (PDF)

Reading Questions for 5d

Class 5 Slides, cont'd (PDF)

Class 5 Slides, cont'd with Solutions (PDF)
4 C6 Expectation, variance, law of large numbers and central limit theorem

6a: Expectation, Variance and Standard Deviation for Continuous Random Variables (PDF)

Reading Questions for 6a

6b: Central Limit Theorem and the Law of Large Numbers (PDF)

Reading Questions for 6b

6c: Appendix (PDF)

Class 6 Slides (PDF)

Class 6 Slides with Solutions (PDF)

Problem Set 4 (PDF)

Problem Set 4 Checker

Problem Set 4 Solutions (PDF)
C7 Joint distributions: independence, covariance and correlation

7a: Joint Distributions, Independence (PDF)

Reading Questions for 7a

7b: Covariance and Correlation (PDF)

Reading Questions for 7b

Class 7 Slides (PDF)

Class 7 Slides with Solutions (PDF)
S4 Covariance, correlation, CLT    
5 C8 Review for exam 1

Class 8: Exam Review (PDF)

Class 8: Exam Review Solutions (PDF)

Class 8 Slides (PDF)

Class 8 Slides with Solutions (PDF)

  
C9 Exam 1      
Statistics: Bayesian Inference
5 C10 Introduction to statistics; maximum likelihood estimates

10a: Introduction to Statistics (PDF)

Reading Questions for 10a

10b: Maximum Likelihood Estimates (PDF)

Reading Questions for 10b

Class 10 Slides (PDF)

Class 10 Slides with Solutions (PDF)

  
6 C11 Bayesian updating with known discrete priors

11: Bayesian Updating with Discrete Priors (PDF)

Reading Questions for 11

Class 11 Slides (PDF)

Class 11 Slides with Solutions (PDF)

Problem Set 5 (PDF)

Problem Set 5 Checker

Problem Set 5 Solutions (PDF)
C12 Bayesian updating: probabilistic prediction; odds

12a: Bayesian Updating: Probabilistic Prediction (PDF)

Reading Questions for 12a

12b: Bayesian Updating: Odds (PDF)

Reading Questions for 12b

Class 12 Slides (PDF)

Class 12 Slides with Solutions (PDF)
S5 Bayesian updating    
7 C13 Bayesian updating: continuous prior, discrete data

13a: Bayesian Updating with Continuous Priors (PDF)

Reading Questions for 13a

13b: Notational Conventions (PDF)

Class 13 Slides (PDF)

Class 13 Slides with Solutions (PDF)

  
C14 Beta distributions: continuous data

14a: Beta Distributions (PDF)

14b: Continuous Data with continuous Priors (PDF)

Reading Questions for 14a and 14b

Class 14 Slides (PDF)

Class 14 Slides with Solutions (PDF)
S6 Continuous data, continuous priors    
8 C15 Conjugate priors; choosing priors

15a: Conjugate Priors: Beta and Normal (PDF)

Reading Questions for 15a

15b: Choosing Priors (PDF)

Class 15 Slides (PDF)

Class 15 Slides with Solutions (PDF)

Problem Set 6 (PDF)

Problem Set 6 Checker

Problem Set 6 Solutions (PDF)

Problem Set 6 R Code (R)
C16 Probability intervals

16: Probability Intervals (PDF)

Reading Questions for 16

Class 16 Slides (PDF)

Class 16 Slides with Solutions (PDF)
S7 No studio    
Statistics: Frequentist Inference—Null Hypothesis Significance Testing (NHST)
9 C17 Frequentist methods; NHST

17a: The Frequentist School of Statistics (PDF)

17b: Null Hypothesis Significance Testing I (PDF)

Reading Questions for 17b

Class 17 Slides (PDF)

Class 17 Slides with Solutions (PDF)

Problem Set 7 (PDF)

Problem Set 7 Checker

Problem Set 7 Solutions (PDF)
C18 NHST II: Significance level, power, t-tests

18: Null Hypothesis Significance Testing II (PDF)

Reading Questions for 18

Class 18 Slides (PDF)

Class 18 Slides with Solutions (PDF)
S8 Rejection regions; NHST    
10 C19 NHST III: Gallery of tests

19: Null Hypothesis Significance Testing III (PDF)

Reading Questions for 19

Class 19 Slides (PDF)

Class 19 Slides with Solutions (PDF)

Problem Set 8 (PDF)

Problem Set 8 Checker

Problem Set 8 Solutions (PDF)
C20 Comparison of Bayesian and frequentist inference 20: Comparison of Frequentist and Bayesian Inference (PDF)

Class 20 Slides (PDF)

Class 20 Slides with Solutions (PDF)
S9 NHST; comparing frequentist and Bayesian methods    
11 C21 Review for exam 2  

Class 21 Slides (PDF)

Class 21 Solutions File (PDF)

  
S10 Exam 2    
Statistics: Confidence Intervals; Regression
12 C22 Confidence intervals for normal data

22: Confidence Intervals Based on Normal Data (PDF)

Reading Questions for 22

Class 22 Slides (PDF)

Class 22 Slides with Solutions (PDF)

Problem Set 9 (PDF)

Problem Set 9 Checker

Problem Set 9 Solutions (PDF)
C23 Confidence intervals II

23a: Confidence Intervals: Three Views (PDF)

23b: Confidence Intervals for the Mean of Non-normal Data (PDF)

Class 23 Slides (PDF)

Class 23 Slides with Solutions (PDF)
S11 Confidence intervals III    
13 C24 Bootstrap confidence intervals

24: Bootstrap Confidence Intervals (PDF)

Reading Questions for 24

Class 24 Slides (PDF)

Class 24 Slides with Solutions (PDF)

  
C25 Linear regression

25: Linear Regression (PDF)

Reading Questions for 25

Class 25 Slides (PDF)

Class 25 Slides with Solutions (PDF)
S12 Bootstrapping; linear regression    
14 C26 Review for final exam  

Class 26 Slides (PDF)

Class 26 Problems File (PDF)

Class 26 Solutions File (PDF)

  
C27 Review for final exam, cont'd  

Class 27 Slides (PDF)

Class 27 Problems File (PDF)

Class 27 Solutions File (PDF)
15   Final exam