200.314 Advanced Statistical Methods

Steven Yantis

Fall, 2008

V. Kandinsky, Composition viii (1923)

Meets Tues and Thurs 1:30-2:45pm in 233 Ames Hall

Instructor: Steven Yantis
email: yantis@jhu.edu
Office hours: Tues 1pm and by appointment

TA: Ben Rosenau <brosenau@jhu.edu>

This course is the first half of the graduate statistics sequence. The goals are (1) to introduce elementary concepts in probability theory and statistics that are important for describing and interpreting quantitative data, and (2) to develop skills in analyzing and thinking critically about empirical data. We will cover probability theory, random variables, probability distributions, signal detection theory, hypothesis testing, t-tests, nonparametric tests, bootstapping and resampling, one- and two-way analysis of variance, correlation, and simple linear regression. You will learn how to perform simulations using MATLAB both to clarify concepts and to perform statistical analysis.

Required Text:
Hays, W.L. (1994). Statistics (5th edition). Belmont, CA: Wadsworth.
ISBN 0-03-074467-9

Optional Text:
Pratap, R. (2005). Getting Started with MATLAB 7: A Quick Introduction for Scientists and Engineers. Oxford University Press.

  HANDOUTS & HOMEWORK

Additional Readings:

Platt (1964) Strong inference. Science, 146, 347-353.

Chamberlain (1965) The method of multiple working hypotheses. Science, 148, 754-759

Loftus, G. (1996). Psychology will be a much better science when we change the way we analyze data. Current Directions in Psychological Science, 5, 161-171.

Wickens, T. D. (2002). Elementary Signal Detection Theory. New York: Oxford University Press. [Chap 1; Chap 2 (sections 2.1-2.3); Chap. 3 (sections 3.1-3.3)]

Swets, J.A., Dawes, R.M., & Monahan, J. (2000). Psychological science can inprove diagnostic decisions. Psychological Science in the Public Interest, 1, 1-26. [This reading is optional but you should read it.]

Howell, D.C. (2002). Statistical Methods for Psychology, Chapter 18. Resampling and Nonparametric Approaches to Data (pp. 692-719).

Byrne, M.D. (1993). A better tool for the cognitive scientist’s toolbox: Randomization statistics. IN W. Kintsch (Ed). Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 289-293). Mawah, NJ: Erlbaum.

Grading:
30% Homework
35% Midterm Exam
35% Final Exam
Homework:
There will be weekly homework assignments. You are encouraged to discuss the homework assignments with classmates in advance of completing them; however, the homework assignments must reflect your own work and should not be completed in group sessions. Generally, the assignment will be given out on Wednesday and due back on the following Tuesday at the beginning of class. We will attempt to return graded homework when new homework is assigned. In order to maintain this rapid turn around, late assignments will not be accepted.
Exams:
There will be two exams in the course, a midterm and a final. Each exam will have a take-home portion and an in-class portion. Each exam will cover the relevant material from lectures, readings, and homework. Exams are to be completed without discussion or collaboration. The final is cumulative but with an emphasis on the second half of the course.

Course Schedule:

Lecture notes for each week will be made available prior to class.
Go to Handouts to download file.

Week

Dates

Topic
Homework

Readings

1-2

Sept 4
Sept 9-11

  • Introduction to course
  • Probability theory
  • MATLAB basics
see Handouts & Homework page

 Chamberlin (1965)
Platt (1964)
Hays Ch. 1
Review Appendix E.1-E.13

2

Sept 16-18
  • Probability Distributions
  • Random Variables
  • Counting Rules
  • Binomial Distribution
 


Hays Ch. 2, 3

3

Sept 23-25
  • Central Tendency and Variability
  • Sampling distributions
 

Hays Ch. 4
Hays Ch. 5 (sections 5.1-5.10 only)
Review Appendix A
Sampling Disn Demo

4

Sept 30-Oct 2
  • Gaussian (Normal) Distribution
  • Central Limit Theorem
  • Confidence Intervals
  

Hays Ch. 6

5

Oct 7-9
  • Signal Detection Theory
  

Wickens (2002)
Swets et al. (2000)
SDT demo

6

Oct 14-16
  • Hypothesis Testing
  • In-class part of midterm exam (Thurs 10/16)
  • Distribute take-home part of midterm exam
  

Hays Ch. 7
Loftus(1996)

 

Oct 20

MIDTERM EXAM DUE at noon

 

7

Oct 21-23
  • Inferences about population means: t-test
  • Confidence intervals revisited
  • Nonparametrics and resampling
 

Hays Ch. 8
Howell Ch. 18
Howell's Resampling page
Byrne(1993)

8

Oct 28-30
  • Chi-square and F distributions
  • General Linear Model
 

Hays Ch. 9,10
F calculator

9

Nov 4-6
  • Analysis of Variance
  • Contrasts
  

Hays Ch. 10, 11
10
Nov 11
  • Factorial ANOVA and contrasts
  
Hays Ch. 12
2-way ANOVA demo
 
Nov 13-18
NO CLASS (Psychonomics/SfN)
    

11

Nov 20-25
  • Linear Regression and correlation
  
Hays Ch. 14, sections 0-10, 14-15, 21-24

Restriction of range demo
Regression demo
 
Nov 27
NO CLASS (Thanksgiving)
    

12

Dec 2-4
  • Wrap-up
  • In-class part of final exam (Dec 4)
  • Distribute take-home part of final exam

 

  

 
 
Dec 8
FINAL EXAM DUE at noon
    

Last modified 9/2/2008