200.314 Advanced Statistical Methods

Fall, 2009

V. Kandinsky, Composition viii (1923)

Meets Tues and Thurs 9-10:15 am in 233 Ames Hall

Instructor: Steven Yantis
email: yantis@jhu.edu
Office: 228 Ames Hall
Office Hours: Mondays 10-11am and by appointment

TA: Sarah Stamper
email: sstamper@jhu.edu
Office: 137 Ames Hall
Office Hours: Fridays 11am-noon and by appointment

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.

 

Additional Readings:

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

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

Wright, D.B. (2009). Ten statisticians and their impacts for psychologists. Perspectives on Psychological Science, 4, 587-597.

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 Thursday 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.

Week

Dates

Topic
HW due

Readings

1-2

Sept 3
Sept 8-10

  • Introduction to course
  • Probability theory
  • MATLAB basics (Sept 8: Meet in Krieger 309)
HW0
Matlab 0

 Chamberlin (1965)
Platt (1964)
Wright(2009)
Hays Ch. 1
Review Appendix E.1-E.13

2

Sept 15-17
  • Probability Distributions
  • Random Variables
  • Counting Rules
  • Binomial Distribution
HW01
Matlab 1


Hays Ch. 2, 3

3

Sept 22-24
  • Central Tendency and Variability
  • Sampling distributions
HW02

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

4

Sept 29-Oct 1
  • Gaussian (Normal) Distribution
  • Central Limit Theorem
  • Confidence Intervals

HW03
Matlab 2

Hays Ch. 6

5

Oct 6-8
  • Signal Detection Theory
HW04
Matlab 3

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

6

Oct 13-15
  • Hypothesis Testing
  • In-class part of midterm exam (Thurs 10/15)
  • Distribute take-home part of midterm exam

Hays Ch. 7
Loftus(1996)

 

Oct 19

MIDTERM EXAM DUE at noon

 

7

Oct 20-22
  • 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 27-29
  • Chi-square and F distributions
  • General Linear Model

Hays Ch. 9,10
F calculator

9

Nov 3-5
  • Analysis of Variance
  • Contrasts

Hays Ch. 10, 11
10
Nov 10-12
  • Factorial ANOVA and contrasts
Hays Ch. 12
2-way ANOVA demo
 
Nov 17
  • Linear Regression
 
 
Nov 19
NO CLASS (Psychonomics)
 

11

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

Restriction of range demo
Regression demo
 
Nov 26
NO CLASS (Thanksgiving)
 

12

Dec 1-3
  • Wrap-up
  • In-class part of final exam (Dec 3)
  • Distribute take-home part of final exam

 

 
 
Dec 7
FINAL EXAM DUE at noon
 

Last modified 7/31/2009