Soc709

Sociology 709 Spring 2009 Class Schedule

	Tuesdays		Thursdays
Month	Date	Lecture #	Date	Lab #
January	(13)	0	15	No class
	20	1	22	1
	27	2, hw1	24	2
February	3	3	5	3
	10	4	12	4
	17	5, hw2	19	Lecture 6, hw3
	24	9 Review	26	9 Exam
March	3	No class	5	Lab 5 & 6
	17	7	19	7
	24	8	26	8
	31	10	2	10
April	7	11	9	11
	14	No class	16	Lec 12 & 13
	21	Labs 12-13	23	Lecture 15
		14

Final exam: see the official registrar exam schedule:

(http://regweb.oit.unc.edu/calendars/index.php

Spring 2008: Thursday May 1, 4:00 pm.

Note: hw# means that a homework assignment will be passed out, due 1 week later.

Lectures

Lecture 0

Introduction.

Overview: Descriptive analysis, scatterplots and linear relationships

Reading: Weisberg, Applied Linear Regression, Chapter 1 “Scatterplots and Regression”

Lecture 0 slides

Lecture 1

Simple linear regression

Reading: Weisberg, Applied Linear Regression Chapter 2 “Simple Linear Regression” pages 19-27

Lecture 1 slides, handout version

Reference: NWK, Chapter 2

Lecture 2

Inferences in regression analysis

Reading: Weisberg, Applied Linear Regression Chapter 2 “Simple Linear Regression” pages 28-38.

Lecture 2 slides, handout version

Reference: NWK, Chapter 3

Lecture 3

Introduction to matrices (Ch A.6, nwk 6)

Reading: Weisberg, Applied Linear Regression Appendix A.6

Lecture 3 slides, handout version

Lecture 4

Multivariate regression

Reading: Weisberg, Applied Linear Regression Chapter 3 “Multiple Regression”

Lecture 4 slides, handout version

Lecture 5

Making Sense of Regression

Reading: [On reserve] Allison, Multiple Regression: A Primer p. 2-14 (“What is Multiple Regression”) and p.97-108 (How does Bivariate Regression Work?), p.15-48 (“How do I interpret Multiple Regression Results”)

Lecture 5 slides, handout version

Reference: Nwk 10

Lecture 6

Dummy variables and interaction terms.

Reading: Neter, Wasserman, and Kutner, Applied Linear Regression Models, Chapter 10.1-10.3 (on reserve)

Lecture 6 slides, html version

Lecture 7

Statistical theory (A.10)

Reading: Hill, Griffiths, and Judge 1997 Undergraduate Econometrics 66-78 “Properties of the Least Squares Estimators” (on reserve)

Kennedy A Guide to Econometrics, 47-58 (on reserve)

Lecture 7 slides, handout version

Lecture 8

Longitudinal data (fixed effects and random effects)

Reading: Kennedy A Guide to Econometrics, Chapter 17 “Panel Data” (on reserve)

Baum, An Introduction to Modern Data Analysis using Stata, (p.220-230 “panel data models”) (on reserve)

Lecture 9 slides, html version

Lecture 9

Review for the exam

Review guide

Answer key to homework #3 (link will be updated when hw is due)

Spring 2008 Midterm 1

Spring 2008 Midterm 1 Answer Key

Lecture 10

Missing data

Reading: Paul Allison, Missing Data, Chapters 1-3 & 5. (pages 1-14, 30-38), on reserve).

Lecture 10 slides, html version

Lecture 11

(1) Problems with the error term [Heteroskedasticity] and (2) Using sampling weights in data analysis

Reading: Fox, Applied Regression Analysis p.295-307 (on reserve)

Baum, An Introduction to Modern Data Analysis using Stata, (p.133-149, on reserve)

Stata User’s Guide, weights (on reserve)

CPC Stata guide “choosing the correct weight syntax”

Lecture 11

Lecture 12

Multicollinearity

Baum, An Introduction to Modern Data Analysis using Stata, (p.84-87) (on reserve)

Kennedy 205-212 (on reserve)

Lecture 12 slides html

Lecture 13

Influential cases

Reading: Weisberg, Applied Linear Regression Chapter 9 “Outliers and Influence”

Fox, Applied Regression Analysis Chapter 11 (on reserve)

Lecture 13

Lecture 14

Specification and omitted variable bias

Reading: Gronniger, “Familial Obesity As A Proxy For Omitted Variables In The Obesity-Mortality

Relationship”, Demography, Volume 42-Number 4, November 2005: 719-735

[Note: this article is on reserve]

Kennedy, Chapter 5 (p. 81-86, 92-99), Chapter 6 (p.107-109, 114-116) [will be placed on reserve 4/21]

Lecture 14

_________________________________________________________________________________________________

Lecture 15

Review

Labs

2-variable graph + AGIS Ch1-2

Lab 2

Lab 2

AGIS Ch3-4, simple regression (AGIS 8)

Matrices

AGIS 10.1-10.3

AGIS 10.9-10.10, 10.8

Lab 6

Dummy variables and interaction terms

Lab 6, html version

Lab 7

Empirical examples of asymptotic normality

Lab 7, html version

Lab 8

Longitudinal data, fixed- and random-effects models.

html version (remember to download data, see link on the lab)

Lab 9

Exam

Lab 10

Missing data

html version (remember to install ICE and download the data, see link on the lab)

Lab 11

Dealing with heteroskedasticity and clustered data

html version

Labs 12-13

Identifying violations of the assumptions: heteroskedasticity, multicollinearity, and influential cases.

html version