Sanda Kaufman's Quantitative Research Methods I Schedule

Quantitative Research Methods I

computer labs

qm page

you are visitor # since 1.5.07

UST 803

Course Schedule by Week

For accuracy and changes, always rely on the syllabus and other materials distributed in class.

WEEK #	WEEK #	WEEK #	WEEK #
1 2 3 4	5, Midterm 1 6 7 8	9 10 *11, Midterm 2* 12	13 14 15 16, Final

*WEEK 1:*	Introduction; The nature of regression analysis
HW1: 1.6, 1.7, 2.4, 2.8.2.9, 2.12, 2.14, 2.16	Gujarati Chapters 1, 2


Encyclopedia Britannica:: John Maynard Keynes Statistics Glossary: Basic Definitions: Variables Measurement scales	The Knowledge Base (for review of some stats basics): Types of data; The language of variables; The unit of analysis; Measurement; Levels of measurement Qualitative research	Math review Karl Pearson Sir Francis Galton Positivism HyperStat: Introduction Summation notation Reporting Measurement scales

Week

*WEEK 2:*	Probability distributions
HW2: 3.8, 3.12, 3.13, 3.15, 4.10, 4.11, 4.13, 4.16, 4.20	Gujarati Chapters 3, 4


HyperStat Simple probability Conditional probability Binomial distribution Normal distribution: What is it? And Why is it important? Standard normal distribution Area under portions of the curve Normal approximation simulation	K. Dear: Introduction to Probability Normal distribution B. Zielman: Central Limit Theorem Galton's Quincunx Let's make a deal 1 "Let's make a deal" 2

Week

*WEEK 3:*	Statistical inference; Basic ideas of linear regression
HW3: 5.6, 5.8, 5.9-11, 5.13-14, 5.16, 5.18, 5.20, 5.21	Gujarati, Chapter 5, 6


David Stockburger: Correlation Jan De Leeuw: Analysis of a pair of variables Correlation calculator (go to statistical calculators) David Lane: Simulation Guessing correlations

Week

*WEEK 4:*	Basic ideas of linear regression (cont.); Hypothesis testing
Computer lab HW:	Gujarati Chapters 6, 7


Friedrich Gauss Encyclopedia Britannica: HyperStat: Computation routines	Knowledge Base: The General Linear Model; Establishing cause-effect relationships; Stanton: Linear regression	Lethen: Simple linear regression Buck: Simple regression Dear: regression TexaSoft: Simple Linear regression

Week

*WEEK 5:*
No class	Prepare for Midterm 1

*WEEK 6:*
MIDTERM 1 (class time, open book) No HW for next week


Knowledge Base: Sampling; Sampling terminology; Statistical terms for sampling;	Regression to the mean Probability sampling; Nonprobability sampling design David Stockburger: Sampling distributions

Week

*WEEK 7:*	Multiple regression
Computer lab HW:	Gujarati Chapter 8


HyperStat: Sampling distributions definition Sampling distribution of the mean Standard error	Area under sampling distribution Sampling distribution simulation Point estimation	Confidence intervals Confidence intervals general formula K. Dear: Sampling distribution of the mean

*WEEK 8:*
No class

Week

*WEEK 9:*	Functional forms, Dummy variables
HW:	Gujarati Chapters 9, 10


HyperStat: Confidence interval for proportion Standard deviation Variance Confidence interval simulation	Knowledge Base: The planning-evaluation cycle Developing an evaluation culture Evaluation; The basic evaluation model Hypotheses	HyperStat: Logic of hypothesis testing: ruling out change Null hypothesis Steps Why null hypothesis is not accepted At what level is the null rejected? Statistical and practical significance

Week

*WEEK 10:*	Model selection
Computer lab HW:	Gujarati Chapter 11

Week

*WEEK 11:*	Multicollinearity
HW:	Gujarati Chapter 12

*WEEK 12:*
HW7: MIDTERM 2 (class time, open book)


	VassarStats, Richard Lowry Chi square for Contingency Table Keith Dear: Inference for count data

Week

*WEEK 13:*	Heteroscedasticity
Computer lab HW:	Gujarati Chapter 13


HyperStat: Introduction Calculations Assumptions Reporting results

Week

*WEEK 14:*	Autocorrelation
HW:	Gujarati Chapter 14


Knowledge Base: Dummy variables Multiple regression	Knowledge Base: Problem formulation Research problem formulation Types of research questions	Knowledge Base: Positivism & Postpositivism

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

*WEEK 15:*
HW:	Gujarati Chapter


Knowledge Base: Advances in quasi-experimental thinking Deductive & inductive thinking

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

WEEK 16

FINAL EXAM (Tuesday, class time, open book) FInal exam grades

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Web materials: Get some critical skills from the University at Albany's Internet Tutorials

Part of UST 803 is a computer lab where we will learn to work with the software MathCad which is useful for simulating and visualizing data sets. You should save your work on the diskette. At the end of the project each student will hand in the diskette with all the programs and a printout of the results.

LAB#1: Tutorial and Two-Variable Regression. This is a .pdf version of LAB#1
LAB#2: Monte Carlo Simulation of the Two-Variable Regression. This is a .pdf version of LAB#2
LAB#3: Mean Prediction, Confidence Interval. This is a .pdf version of LAB#3
LAB#4: Cobb-Douglas Production Function. This is a .pdf version of LAB#4

This site has been prepared by Sanda Kaufman (.)
Please contact her with any comments.