2 September 2003

Statisticsfor Behavioral Scientists

Psychology W1610x

PROFESSOR John Daws

johndaws@psych.columbia.edu

LECTURES Mondays, 6:10 to 8:00614 Schermerhorn

OFFICE HOURS Mondays, 4:30 to 6:00366 Schermerhorn

Statistics for Behavioral Scientists

Labs

SECTION 1 Tuesdays 2:10 to 4:00 pm

200B Schermerhorn

SECTION 2 Tuesdays 4:10 to 6:00 pm

200B Schermerhorn

SECTION 3 Tuesdays 6:10 to 8:00 pm

200B Schermerhorn

Textbook

David Moore and George McCabe,

Introduction to the Practice of Statistics (4th ed.)

You should read the assigned section(s) of the text before each lecture. I recommend that you read the text twice, once before lecture and again after. The first time, read quickly to familiarize yourself with the topics. Expect the lecture to help you understand the material. Ask questions in class if it doesn't. After lecture, read the chapter again to strengthen your grasp of the material. If you still have questions, ask them at the beginning of the next lecture or in lab.

Grading: Lecture Participation

5 points

I expect you to attend the lectures and the labs, to take part in class discussions, and to ask questions. The students who speak up during lecture help set the pace.

Grading: Lab Participation

5 points

You are also expected to attend and participate in a lab section. In lab, you will be using Excel and SPSS to help you understand the basic concepts of statistics and to apply these concepts to data-analysis projects. The lab will also be a time when you can ask questions about the homework, or about something you've read in the textbook or heard in lecture. (In addition to the scheduled lab session, you will be able to work on your own during Open Lab Hours.)

Grading: Homework

20 points

There are 12 sets of homework problems to be worked. Each set is worth two points. Because the primary purpose of the homework is to help you stay abreast of topics as we cover them, each homework must be turned in no later than the beginning of the lecture (Mondays at 6:10) on the assigned date. No late homework will be accepted, so keep in mind that it's much better to turn in something rather than nothing. Note that you can still earn all 20 points even if you fail to turn in one or two of the homeworks.

You will need a calculator or a computer on some of the homework problems.

I encourage you to form study groups. Working together on homework is a good idea; outright copying is not. The work you turn in should indicate your own ability to work the problems.

Grading: Quizzes

45 points

There are three quizzes, each worth 15 points. Questions may come from the lectures, the labs, or the text. Bring your calculator.

The quizzes will be given during lab times (Tuesdays at 2:10, 4:10, or 6:10) on 7 October, 28 October, and 2 December. If you know that you will have to be absent on any of these days, please speak to me as soon as possible. Otherwise, if you miss a quiz because you are sick or for some other non-trivial reason, please notify me as soon afterward as possible and arrange to take a make-up quiz. You may be asked to bring a note from a physician or a dean.

If you believe that an error is made in grading a quiz, please give your teaching assistant or me a written explanation of what you believe the error to be, along with a photocopy of the quiz. Oral requests for re-grading will be ignored.

Grading: Final Exam

25 points

The final exam will cover all of the material of the course. It will be given on Monday, 15 December. A single alternative date might be arranged, if necessary.

Final Grades

A 90 to 100

B 80 to 90

C 70 to 80

It is possible that these thresholds could be lowered (for example, a grade of 89 might be an A-minus), but they will not be raised. Students with fewer than 50 points should not expect to pass.

Sept8

Lecture 1

Summarizing data9

Lab 1

Intro to the Mac labIntro to Excel

15Lecture 2

The normal curve16

Lab 2

Histograms, center, and spread

22Lecture 3

Relationships of two variables23

Lab 3

Scatterplots and correlationIntro to SPSS

29Lecture 4

Linear regression30

Lab 4

Regression in Excel

Oct

7QUIZ 1

Descriptive Statistics

Oct6

Lecture 5

Probability

13Lecture 6

Random variables14

Lab 5

Summarizing data which don’t exist

20Lecture 7

X–bar and the CLT21

Lab 6

Envisioning the central limit theorem

28 QUIZ 2

Probability and Sampling

Oct27

Lecture 8

Interval estimation

Nov

10

Lecture 9

Statistical decision making11

Lab 7

Confidence

17Lecture 10

One- and two-sample t tests18

Lab 8

Power

24Lecture 11

The analysis of variance25

Lab 9

t tests and F tests in SPSS

Dec

2QUIZ 3

Hypothesis Testing

Dec1

Lecture 12

Categorical data analysis

8 Review

15 FINAL EXAM

Finishing Up