Statistics 101
Introduction to Quantitative Methods
for Psychology and the Behavioral Sciences
Fall 2009
Course Objective: Statistics 101 introduces the basic concepts of statistics and the statistical methods
that are most commonly used in psychology and in the social and behavioral sciences. There is no prerequisite. The first half of the course introduces descriptive statistics and the inferential statistical methods of
confidence intervals and significance tests, applied to means and proportions. The second half introduces
bivariate and multivariate methods, emphasizing contingency table analysis, regression, and analysis of
variance.
Instructor: Alan Agresti, Science Center 300b, e-mail AA@STAT.UFL.EDU, phone (617) 495-8711
Office Hours: Tentatively Tuesday and Thursday 11:30-12:30, Tuesday 4-5 pm, and by appointment.
Teaching Fellows: Jonathan Hennessy (jhenness@fas.harvard.edu) and Joseph Kelly (kelly2@fas.harvard.edu)
Office hours and help sessions to be announced
Course web page: You can access this at my.harvard.edu, and also at www.stat.ufl.edu/∼aa/harvard/
Text: Statistical Methods for the Social Sciences, by A. Agresti and B. Finlay, Pearson Prentice-Hall
4th edition, 2009
Exams and grades: Two exams during the term and the final exam each contribute to 20% of the final
grade. Make-ups will not be given unless arrangements have been made prior to the exam, and then only
for illness or family emergencies. A project (details later, for the reading period) also counts 20%, and the
remaining 20% is based on homework exercise solutions.
Exam Dates (tentative):
Exam 1
Exam 2
Final Exam
October 1
November 5
December final exam period
Homework: Exercises from the textbook will be assigned in 10 homework assignments. These are designed
to help you to improve your understanding of the course material and to help you prepare for exams, which
will contain similar questions. In the Thursday lecture you will be assigned a set of exercises. You should
give your solutions to your teaching fellow by 4 pm, at the latest, on Friday of the following week. (You can
leave this in the course mailbox in the Statistics Department office on the 7th floor of the Science Center.)
Late homework solutions will not be accepted. You are welcome to discuss the homework exercises with
other students and your teaching fellow, but you must write your final answer yourself, in your own words.
Any software output that you submit as part of your solutions must come from work that you have done
yourself. Selected exercises will be graded by the teaching fellow and will count for 20% of your final grade.
Solutions will be posted at the course home page. Teaching fellows will have class sessions and office hours
at which you can ask questions about course material, including homework exercises and software, and their
sessions will present extra examples to supplement the basic ones presented in the class lectures.
Software: Some examples in class will use SPSS statistical software, especially for the more computationally
complex methods in the second half of the course. Students are encouraged to become familiar with the
use of SPSS or another software package (such as Stata, SAS, or R) and use it in homework exercises where
the use of software is appropriate. SPSS is available through FAS computing (www.fas-it.fas.harvard.edu).
Outline: The class lectures will cover topics presented in the following text material. On exams you are
responsible for all material presented in lectures plus any extra material that you are assigned to read.
Chapter and Topic
Text Pages
Homework Problems (optional parenthesized)
1. Introduction to Statistics
1-7
2, 3, (5, 17)
2. Sampling and Measurement
11-21
1-3, 7, 13, 14, 27, 34, 36, 37 (5, 24, 38)
3. Descriptive Statistics
Univariate description
31-55
Bivariate description
55-61
6, 10ab, 11, 19, 22, 24, 25, 28, 30, 35 (8, 33, 34ab)
39, 41, 59, 68, 69, 70, 72, 73, 74, 78, (43, 64, 66)
48, 49, 50, (47, 51, 52)
4. Probability & Sampling Distributions
Probability, normal distributions
85-99
8ab, 9ab, 10abc, 11ab, 12ab, 17, 21, 23, 54, 55,
(3, 8c, 9cd, 10de, 11cde, 12cd, 19, 47)
27, 29, 33, 41, 42, 46, 50-53, 57, (36, 54, 55)
5. Inference: Estimation
Confidence interval for proportions
Confidence interval for means
Sample size determination
107-116
116-123
123-129
4, 7, 12, 13, 47, 48, 66-68, 77, (8, 18, 76)
21, 24, 25, 32, 69-71, 73, (22, 28, 54)
35, 41, 44, 62, (34, 38, 39)
6. Inference: Significance Tests
Mean: Steps of significance test
Proportion: Steps of significance test
Decisions and types of errors
143-155
156-159
159-166
166-169
169-173
1-3, 9, 11, 39, 52-55, (6)
15, 40, 42, (14, 16, 21, 65)
17, 18, 24, 25, 45, 50, 51, 59, 61,
(22, 23, 26, 27, 46, 63)
29, 30, 56, (28, 31, 57)
33, 34
7. Comparison of Two Groups
Comparing proportions
Comparing means
Matched samples
183-190
191-193, 197-201
193-197
1, 9, 11, 16, 17, 59, 62, (8, 12, 14, 17, 47, 65)
21, 23, 32b, 49, 50, 60, 61, 63, (5, 6, 19, 22, 55)
26, (27, 28, 31)
8. Association for Categorical Variables
Contingency tables, Chi-squared
Residuals
Summarizing association
221-229
229-233
233-239
1, 3, 5, 9, 10, 29a, (2, 4, 40, 44)
11, 16, (14)
18, 20, 22, (17)
9. Linear Regression and Correlation
Regression model, least squares
Correlation and r 2
Inference, assumption, influence
255-268
269-276
276-289
1, 3, 7, 10, (25, 63, 66)
11-12, 18, 20, 38, 58-61, (13, 67, 68)
29, 32, 42, 50-55
10. Intro. to Multivariate Relationships
Association/causality, statistical control
Relationships, statistical interaction
301-307
307-313
3, 5, 7b, 33, 34, 40, (7a, 39)
11, 14, 16, 32, 38, 42-44, (41, 45)
11. Multiple Regression
Multiple regression and correlation
Inference, interaction, comparing models
321-335
335-346
1, 4-7, 19, 46, 49, 66, (67)
25, 44, 48(a-f), (48(h-k,m,n))
12. Comparing Groups: Anal. of Variance
Comparing means: F test
Multiple comparisons
ANOVA using regression
Two-way and factorial ANOVA
369-376
376-378
378-381
382-391
2, 5, 36, 38, 39, 51, 52, (1, 3, 6, 57)
10, 14, 42, (12, 40, 53)
15, (16)
17, 22, 24, 41, 45, 48, (23, 44)
13-15. Advanced topics (brief intro.)
Quantitative and categorical predictors
Modelling nonlinearity
Logistic regression
413-422
462-473
483-493
Sampling distributions
Finding power and P(Type II error)
Binomial test for small n
73-85