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Econ 290, Statistics

 

 

 

Syllabus, Eco 29000, Spring 2011

Principles of Statistics

Tuesday and Friday 12 – 1:50 NAC 1/203

 

 

Course Description

 

This course is designed to teach you to use the simple statistical tools that form an economist's basic toolbox.  This is a hands-on course where you will work with a lot of real data.  The aim of this course is to get a better understanding of statistics, of how numerical evidence is used and abused, and of how people can torture the numbers to make them appear to support their point of view.  In our world statistics are the first choice for how someone is going to lie to you.  If you know some of the secrets then you will be able to see through other people's lies (and perhaps create some of your own – if you choose to embrace the dark side!)..

 

Textboook

 

This course will use the textbook Applied Statistics for Business and Economics, David Doane and Lori Seward, 3rd edition, McGraw Hill.  If you have an earlier edition, you are responsible for ensuring the concordance to the most recent edition. You may wish to get the accompanying study guide, but it is not necessary.

 

Professor

 

Kevin R. Foster, Department of Economics, The City College of New York, kfoster@ccny.cuny.edu, w: (212) 650-6201, m: (860) 593-7674, office hours Tuesday & Friday 2-3 pm or by appointment, http://www.ccny.cuny.edu/social_science/kfoster/

 

Course Requirements/Prerequisites

 

This course will assume that you've taken the prerequisites, Principles of Micro and Macro.  Calculus is not a prerequisite although I will occasionally use it to help advanced students get a fuller understanding.  I will use other math freely and often.  Stats doesn't require any high-level math (occasionally we will take derivatives or integrate, but only to illustrate a point).  But stats does require a willingness to work with algebra and plow through the applicable formulas.  However, that said, the point of doing it on a computer is so that the machine can do the donkey work while you worry about bigger questions – so what?  why?  what does it mean?  what else do I need to know?  what other hypotheses could present the same pattern?

 

Educational Outcomes

 

Students will be able to apply mathematically rigorous analysis to topics such as hypothesis testing, common probability density functions, and regression analysis.

 

Grading

 

Course grades are determined by three factors: your scores on the final and the midterm, your demonstrated skill at using statistical analysis in a final project, and your scores on the homework assignments.  The final has a 30% weight, the midterm has a 20% weight, the project has a 30% weight, and homework gets 20%.  There is no BS factor of effort or any other unobservable will-o-wisps – the weightings sum to 100.  Your grade is determined entirely on observed performance.

 

You have the option of forgoing the homework assignments and having your grade determined only by exams and project.  This is unwise.  You must submit the online form to me early in the semester.

 

Grades will be posted on the Blackboard page, so that you can check your progress and determine what grade you can expect to receive. In this public grade posting, you will be identified only by the last 4 digits of your ID number (if you wish to choose some other 4-digit identifier, email me).

 

Time Requirements

 

You should expect to spend 10-12 hours per week on this class.  My simple calculation is that a student who is going to school "full time" takes 4 or 5 classes.  Someone who works fulltime at a job works 40-50 hours per week.  So about 10 hours per week is a good estimate (this class is 4 credits so it will take a bit more).  If you don't put in that much work then you can't expect to get a good grade.  (This is confirmed by research; on average a student studying one more hour per week can raise her term GPA by 0.36 – from a B to B+, for example.  Stinebrickner & Stinebrickner, 2008.  BE Journal of Econ Analysis & Policy, 8(1).)

 

Final Project

 

You will work with a small group  of fellow students to write a project to analyze a question using one of the datasets that we'll be working with.  You will make a presentation about this project in class; the presentation counts as homework and then the written project has a separate grade.  This will require you to use statistical analysis software with a large dataset (i.e. thousands of observations).  More details will be given later in the course.

 

Course Material

 

Homework and basic course documents will be on the class page, publicly accessible from my web page (http://www.ccny.cuny.edu/social_science/kfoster/).  Readings and datasets will be on InYourClass.com (login required).  Some of the homeworks will be available on the Blackboard course page (login required).  I will periodically send emails to the class via Blackboard so you must keep your CCNY email updated.

 

Computer Use

 

This course will use SPSS, data analysis software that is commonly used in business.  You are not required to have previous experience with programming although that would be useful.

 

TA Help

 

There will usually be a TA available in the economics computer lab (NAC 6150) on Friday.  They won't do your homework for you, though!

 

Additional Reading

 

If you begin a love affair with Statistics and want to read more, here are some suggestions:

·         Leonard Mlodinow, The Drunkard's Walk: How Randomness Rules Our Lives

·         Edward R. Tufte The Visual Display of Quantitative Information, Visual Explanations: Images and Quantities, Evidence and Narrative (in library)

·         Howard Wainer, Graphic Discovery: A Trout in the Milk and Other Visual Adventures

·         David Salsburg, Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

·         Stock & Watson, Introduction to Econometrics and Kennedy, A Guide to Econometrics

·         Jane E. Miller, The Chicago Guide to Writing about Numbers (in library)

·         John W. Tukey, Exploratory Data Analysis (in library)

·         Stephen Stigler, Statistics on the Table (in library) and The History of Statistics: The Measurement of Uncertainty before 1900 (in library)

·         Dierdre McCloskey , Economical Writing and The Rhetoric of Economics (in library)

Weekly Topics:

Principles of Statistics, Eco290, Fall 2010

Kevin R Foster, CCNY

 

 

Week

Date

Topics

Chapter(s) in text

1

Jan 26

Data, Descriptive Statistics

1, 2, 3, 4

2

Feb 1, 4

SPSS

additional online

3

Feb 8

Probability

5

 

Feb 11

No class – college closed

 

4

Feb 15, 18

Discrete Probability Distributions

6

5

Feb 22, 25

Continuous Probability Distributions

7

6

Mar 1, 4

Sampling Distributions & Estimation

8

7

Mar 8, 11

One-sample Hypothesis Tests

9

8

Mar 15, 18

Review; Exam on 18th

Ch 1-9

9

Mar 22, 25

Two-sample Hypothesis Tests

10

10

Mar 29, Apr 1

Analysis of Variance

11

11

Apr 5, 8

Simple Regression

12

 

Apr 12, 15

Multiple Regression

13

 

Apr 19, 22

No class

 

 

Apr 26

No class

 

 

Apr 29

Advanced Topics

Additional online

12

May 3, 6

Presentations on Final Project

 

13

May 10, 13

Presentations on Final Project

 

14

May 17

Presentations on Final Project

 

 

sometime May 20-27

Final Exam

 

 

May 27

deadline for final project

 


Chapters refer to Applied Statistics for Business and Economics, David Doane and Lori Seward, 3rd edition, McGraw Hill.

 

There will also be lecture notes available online.  Exams will cover material in both textbook and lecture.

 

Deviations from the schedule will be announced in class.

 

The exam dates are given above. You must take the exams at the scheduled times. No excuses.

 

 

 

Academic Integrity

 

The CCNY Faculty Senate has recommended that every course syllabus include this notice:

CUNY Policy on Academic Integrity

As stated in the CUNY Policy on Academic Integrity: 'Plagiarism is the act of presenting another person's ideas, research or writings as your own. The following are some examples of plagiarism: