Introduction to Statistics for the Social Sciences

Yale University, Fall 1997

Political Science 452a / EP&E 203a / Statistics 102a

Professor Martin Gilens

70 Sachem St. #301

432-5261

This is a team-taught course that covers basic statistics as used in the social sciences, especially political science and sociology. It is part of a series of introductory statistics courses taught jointly by the Statistics Department and the Political Science, Forestry, Biology, and EP&E programs. Tuesday meetings will consist of lectures by Professor Barron of the Statistics Department which will focus on the fundamentals of statistical theory. Thursday meetings with Professor Gilens will be used to present additional statistical material, to study applications of statistical techniques in the social sciences, and to explore the computer analysis of "real data" (primarily drawn from attitude surveys).

Tuesday Lectures, 1:00-2:15
Professor Andrew Barron, Statistics
Dunham Laboratory 220
Thursday Section, 1:00-2:15
Professor Martin Gilens, Political Science
140 Prospect St., room 102a

Course requirements include weekly assignments, some of which will involve computer exercises with the MINITAB program, and in-class midterm and final exams. Homework will be assigned every Thursday and due in the box in the statistics department labeled "Stat102/PL452/EPE203" (at 24 Hillhouse) by 4:00PM on the following Tuesday. (Assignments will be returned at the beginning of Thursday class, one week after they are assigned). Grades will be based 25% on homework assignments, 30% on the midterm, and 45% on the final exam.

The primary textbook for this course is Introduction to the Practice of Statistics by David S. Moore and George P. McCabe, and is available at the Yale Co-op in the Chapel Square Mall and at the Yale Book Store on Broadway.

Tuesday Thursday

4 Sept. Chapter 1. Course Preview. Displaying distributions. Stemplots, histograms.

9 Sept. 11 Sept. Chapter 1. Center and spread. Median, quartiles, boxplots. Mean, standard deviation. Normal distribution.

16 Sept. 18 Sept. Chapter 2. Displaying bivariate distributions. Scatterplots, linear fits, exponential fits.

23 Sept. 25 Sept. Chapter 2. Correlation and regression. Simpson's Paradox. Two-way tables.

30 Sept. 2 Oct. Chapter 3. Sampling and experimental design.

7 Oct. 9 Oct. Chapter 4. Probability. Population mean, standard deviation. Conditional probability, independence.

14 Oct. 16 Oct. Chapter 5. Stability and variability of counts and averages. Binomial distribution. Normal distribution. Central limit approximation.

21 Oct. 23 Oct. Chapter 6. Concepts of inference. Estimates, standard errors. Confidence intervals. Hypothesis tests.

28 Oct. 30 Oct. Review of Chapters 1-5, Midterm Exam.

4 Nov. 6 Nov. Chapter 7. Inference for mean values. Comparing two means.

11 Nov. 13 Nov. Chapter 8. Inference for proportions. Chi-square tests. Two-way tables.

18 Nov. 20 Nov. Chapter 9. Inference for regresssion. Standard errors, confidence intervals. Prediction intervals.
25 Nov. 27 Nov. Fall Recess

2 Dec. 4 Dec. Chapter 9. Inference for regression. Test for linear relationship. Tests for inclusion of variables. Final prediction error.

18 Dec. Final Exam (9:00 AM)

Tuesday	Thursday
	4 Sept.	Chapter 1. Course Preview. Displaying distributions. Stemplots, histograms.
9 Sept.	11 Sept.	Chapter 1. Center and spread. Median, quartiles, boxplots. Mean, standard deviation. Normal distribution.
16 Sept.	18 Sept.	Chapter 2. Displaying bivariate distributions. Scatterplots, linear fits, exponential fits.
23 Sept.	25 Sept.	Chapter 2. Correlation and regression. Simpson's Paradox. Two-way tables.
30 Sept.	2 Oct.	Chapter 3. Sampling and experimental design.
7 Oct.	9 Oct.	Chapter 4. Probability. Population mean, standard deviation. Conditional probability, independence.
14 Oct.	16 Oct.	Chapter 5. Stability and variability of counts and averages. Binomial distribution. Normal distribution. Central limit approximation.
21 Oct.	23 Oct.	Chapter 6. Concepts of inference. Estimates, standard errors. Confidence intervals. Hypothesis tests.
28 Oct.	30 Oct.	Review of Chapters 1-5, Midterm Exam.
4 Nov.	6 Nov.	Chapter 7. Inference for mean values. Comparing two means.
11 Nov.	13 Nov.	Chapter 8. Inference for proportions. Chi-square tests. Two-way tables.
18 Nov.	20 Nov.	Chapter 9. Inference for regresssion. Standard errors, confidence intervals. Prediction intervals.
25 Nov.	27 Nov.	Fall Recess
2 Dec.	4 Dec.	Chapter 9. Inference for regression. Test for linear relationship. Tests for inclusion of variables. Final prediction error.
	18 Dec.	Final Exam (9:00 AM)