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```ECON 201: Statistical Analysis of Economic and Social Data
SDSU, Fall Semester, 2015
Instructor: Ivan Major
Professor of Economics,
Department of Economics, SDSU
e-mail: [email protected]
Office: Nasatir Hall #315
Office hours: Mo, We, 9:00–9:50 a.m.
Lecture: Mo, We, Fri 11:00–11:50 a.m.
Classroom: Hardy Tower #183
Course description
This is an introductory course to statistical methods to be used in analyzing social and economic
data. You will be acquainted with the principles and methods of how to collect information on
your analytic subject and what you need to be careful of. First, you need to learn how to
transform your collected information into data that can be analyzed by graphical and quantitative
methods. You will also study the ways how the collected information can be organized (grouped)
and used for your own research purposes. Next, you learn about graphs and indicators that
represent your data in a concise manner. You will be able to assemble graphs and form indicators
yourself. You will also learn methods of searching for relationships among different groups of
information you had collected. That is, you will become familiar with statistical inference. You
will need some basics in probability calculus so that you fully comprehend the usage and
message of your analytical tools of statistical inference. By the end of the course you will be able
to conduct bivariate regression analysis on your data.
The course requires no more than the knowledge of basic algebra. On a few occasions, I shall
present the deeper methodological (mathematical) background for those interested, but these
presentations will not be part of your exams.
Required reading: David S. Moore and William I. Notz, Statistics: Concepts and Controversies,
8th edition, New York, NY: W.H. Freeman and Co, 2013. (Referred to as M-N in the syllabus)
The book is already available at the SDSU bookstore.
The course has a Web site: http://blackboard.sdsu.edu and look for ECON 201 under my name
where you will find the syllabus and the lecture notes.
Course objectives
This course is about analytical tools rather than economic or social substance. But you will not be
meeting sterile mathematical concepts and formulas. My purpose with this course is as follows:
1
1. By the end of this course you will be able to reason and make your own judgment with the
help of data.
2. You learn to master the tools of elementary statistics, or the methods you can use to find the
underlying patterns in, and the social or political implications of, your data.
3. You acquire basic computer skills so that you can handle and analyze large amounts of
information.
There will be five exams, all closed book and notes. If you complete your five exams
successfully, that will set your final grade. There will not be make-up exams. If you miss two or
more exams you must take a comprehensive final exam on all of the course material.
5 in-class closed book exams
max 20% each
Final grades for this course will be calculated as follows:
98–100%
94–97%
90–93%
85–89%
80–84%
75–79%
70–74%
65–69%
60–64%
50–59%
<50%
A+
A
AB+
B
BC+
C
CD
F
Course schedule
Week 1
Introduction: Where Do Data Come From?
Social Research and Statistics
Brief Introduction to SPSS
2
Week 2
Producing Data: Sampling
What Do Samples Tell Us?
Sample Surveys in the Real World
Week 3
Experiments and Data Ethics
1st exam on Friday of Week 3: Collecting and understanding data; Sampling; Experiments
Week 4
Measurement and Graphs
Week 5
Measures of Central Tendency and Variability
Week 6
Measures of Association for Nominal and Ordinal Variables
2nd Exam on Friday of Week 6: Principles of measurement; Graphs; Measures of CT and VAR;
Measures of Association
Week 7
Probability and statistical analysis
3
Week 8
Normal Distribution and its applications
Week 9
Scatterplots; Correlation
3rd Exam on Friday of Week 9: Probability; Scatterplots; Correlation; Normal distribution and its
applications
Week 10
Regression and Inference
Week 11
Estimation
Week 12
Inferential Statistics 1
4th Exam on Friday of Week 12: Regression; Estimation; Inferential Statistics
Week 13
Inferential Statistics 2