Sociology 360, Statistics for Sociologists I

Lecture 1, 9:30-10:45 TR, 6104 Social Science Building



Prof: Robert M. Hauser TA: Pil Ho Kim

4430 Social Science 7110 Social Science

262-2182 262-3569

E-mail: hauser@ssc.wisc.edu E-mail: pkim@ssc.wisc.edu

Office hours: 2-4:30 Tue, 11-12 Thurs Office hours: 11:00-12:00 Mon

(or by appt, or drop in) 1:00-2:00 Thurs



Labs: 301: 9:55-11:50 am Wed, 1407 Sterling Hall

302: 12:05-2:00 pm Wed, 6109 Social Science.



(Note: Early in the course, lab sessions and some office hours will be held in the Social Science Microcomputer Lab [SSML], Room 3218 Social Science.)



Subject Matter and Objectives:

Sociology 360 is a first course in statistics, covering basic concepts of descriptive and inferential statistics. The topics include graphical displays of data; summary statistics; the binomial and normal distributions; correlation and least squares; simple research designs; probability and random variables; and inferences about means, counts, and simple regression. Students will analyze and display small bodies of data using computers and calculators and will interpret and evaluate research findings.



Prerequisites:

Sophomore standing and basic algebra skills.



Texts:

(1) David S. Moore. The Basic Practice of Statistics. (Second edition) New York: W.H. Freeman, 2000. (BASIC)



(2) Judith M. Tanur, et al. Statistics: A Guide to the Unknown. (Third edition) Belmont, CA: Duxbury Press, 1989. (SAGTTU)



(3) J. Theodore Anagnoson and Richard E. DeLeon. StataQuest 4: Statistics, Graphics, Data Management. Belmont, CA: Duxbury Press, 1997. (SQ4) (Note: This text is packaged with Windows 95 software. If you want to install it on your own computer, be sure that you purchase the right version. A network version is available on PCs at the SSML.)



Advice:

Use e-mail, office hours, and telephone, as well as lectures and labs, to get help when you need it. (Or to offer advice to the instructors when you think they need it.) Also, use the CD-ROM that comes with BASIC, and related sites on the internet.



Lectures:

Lectures will focus on basic concepts and their application as presented in Moore. Notes will be distributed before each lecture. Attendance at all lectures is expected, and it will contribute to the final grade. Students who skip lectures do so at their own risk.



Labs:

Lab sessions will combine instruction and practice in statistical computing with review of the previous week's homework assignment. At the beginning of the term, a great deal more time will be spent on computing, and later in the term, the focus will be on problem-solving. Attendance at all labs is expected, and it will contribute to the final grade. Students who skip labs do so at their own risk.



Examinations:

There will be three, non-cumulative, in-class examinations. The mid-terms will be held the weeks of October 4 and November 8, and the final is on Sunday, December 19. 1999 (10:05 am). Examination questions will mainly be open-ended questions requiring discussion, data analysis and calculation, and/or the selection of appropriate statistical methods.



Problem Sets:

Weekly assignments will be made from all three texts. Assignments for the previous week are due by 12 noon each Monday at Mr. Kim's office, Room 7110 Social Science. (But note the different deadlines for the last two assignments, shown on the schedule below.) Each homework assignment will be given one of two grades: "2" if complete, substantially correct, and well-documented; "1" if incomplete, substantially incorrect, or poorly presented. Late assignments will be accepted, but given a grade no higher than "1." Individual problems will not be marked, but correct answers will be provided and discussed, and students are encouraged to consult with the Professor and TA to discuss problems. Problem sets that are exceptionally poor or show little effort may be rejected by the TA and may be resubmitted to earn a grade no higher than "1." See Professor Hauser about grades: The TA's job is to teach, not to evaluate.



Computer Use:

Problems for which use of a computer is recommended will be assigned from

the StataQuest 4 text and the main texts. Use of the StataQuest 4 program is recommended, but not required. A more powerful alternative is Stata, which is available on many university computers. However, course staff are not responsible for supporting the use of other software.



Calculator Policy:

Students will need an inexpensive scientific calculator for problem sets and exams. Enough hand-work must be shown to demonstrate understanding. The calculator must be able to compute standard deviations and simple (two-variable) regressions. Do not wait for the first exam to learn how to use your calculator!



Grading:

Forty-five percent of the grade will be based on the three exams (10%, 15%, and 20%). Five percent is based on class attendance and participation, and 35% will be based on homework, including computer problems. The remaining 15% of the grade will be based on a final, take-home assignment (due at the beginning of the final exam on December 19), which will require analysis of data supplied by the instructor and a brief report about that analysis. To receive a final grade, students must take all three exams and complete all problem sets, including computer problems and the final exercise in data analysis.



Departmental Notice:

The Department of Sociology regularly conducts student evaluations of all professors and teaching assistants near the end of the semester. Students who have more immediate comments, complaints, or concerns about Sociology 360 should report them either to Professor Hauser or Mr. Kim, or else to Professor Nora Schaeffer, Associate Chair, 2440 Social Science (schaeffer@ssc.wisc.edu) or to Professor Charles Halaby, Chair, 8128 Social Science (halaby@ssc.wisc.edu).

Lecture Week Title Reading (Basic)  Problems
(Basic)
Reading

(SAGTTU)
Problems

(SAGTTU)
Reading

(SQ 4)
Problems

(SQ 4)
  1(9/2) Introduction            
 1 2 (9/7) What is statistics?

Graphing distributions

1.1 2,4,10,12,14, 16,18 188-197

(or 87-92)

1,2,3,5,76

(or 1,2,3)

Ch. 1-3 2.1-2.5, 3.1-3.3 (printed)
 2 2 (9/9) Numeric summaries of distributions 1.2 27,28,30,32,

34,36,40,48

161-169 1,3,4,7,8    
 3 3 (9/14) Normal distribution 1.3 54,56,58,60,

64,68,70

      Ch. 4, Ch. 6 4.1,4.3,4.4, 6.1-6.4
 4 3 (9/16) Scatterplots 2.1 4,14 132-141 1,2,3,5,7,8 Ch. 10 (except pp. 140-141) 10.1,10.4
 5 4 (9/21) Correlation 2.2 18,20,22,28       Ch. 16 (to p. 230) 16.1
 6 4 (9/23) Simple regression 2.3 32,34,40,46, 50 41-52 1,3,5,6 Ch. 10 (140-141) 10.2
 7 5 (9/28) Interpreting regression 2.4 56,58,60,64        
 8 5 (9/30) Producing data: Designing samples 3.1 2,4,6,10,18,

20,22

25-30

1,2,4,5 Ch. 7 7.1,7.2,7.4
 9 6 (10/5) Producing data: Designing experiments, Review 3.2 32,34,38,40,

50

218-226 1,2,4,5,8,9    
 * 6 (10/6-10/7) Exam 1 (lecture and lab)            
 10 7 (10/12) Randomness and probability models 4.1-4.2 2,4,6,10,14,

18,20,22,24,

30,34

151-160 1,2,4,6,7,8 Ch. 8 8.1-8.5
 11 7 (10/14) Sampling distributions 4.3 38,40,42,44,

50,64

       
 12 8 (10/19) General probability rules 5.1 2,4,6,8,10,16

104-112 2,3,4,6,7,8,9 Ch. 9 9.1-9.5
 13 8 (10/21) Binomial distribution 5.2 18,20,22,26,

28,30,36

       
 14-15 9 (10/26, 10/28) Confidence intervals 6.1 2,4,6,8,12,

14,16,18

31-10 1,2,3,4,5,8    
 16-17 10 (11/2, 11/4) Significance tests 6.2, 6.3 26,28,30,32,

34,36,38,40,

44,48,58,62

79-86 1,2,3,5    
  11 (11/9, 11/11) Review and exam 2 (lec)            
 18-19 12 (11/16, 11/18) Inference for the mean 7.1 2,4,6,10,12,

16,20

68-76 1,2,3 Ch. 13 (172-77, 180, 184-87), 15 (204-8, 210-13, 215-18, 219-22) 13.2, check Moore 7.9, 15.1, 15.2, 15.6, 15.8
 20 13 (11/23) Comparing two means 7.2 28,30,32,36,

40,44

53-59 2,3,5 Ch. 13 (178-80), 15 (208-10) 13.1, 13.3, check Moore 7.32, 7.36
 21 14 (11/30) More two-sample tests 7.3, 8.1, 8.2 7.52,7.56,8.2,8.4,8.6,8.8,

8.10,8.12,

8.20,8.24,8.28

115-125 1,4,5,8,9,10 Ch. 5 5.1-5.4
 22 14 (12/2) Inference for regression, Part 1 Ch. 11 2,4,6,12,14,23 261-267 1,3,4 Ch. 17 17.1, 17.2
 23 15 (12/7) Inference for regression, Part 2            
 24 15 (12/9) Inference for counts Ch. 9 (and read Ch. 2.5) 2,4,6,8,20

(from Ch. 9)

       
* 16 (12/14) Review for final examination            

 

Note: Assignments based on lectures 20 and 21 (comparing two means and two-sample tests) are due by noon on Saturday, December 4, 1999, and assignments based on lectures 22 and 23 (inference for regression, parts 1 and 2) are due by noon on Saturday, December 11, 1999.