Stern School of Business
Statistics and Data Analysis
Professor: William Greene, Departments of
Economics and IOMS
BS Ohio State University, 1972 (Operations Research); MA
Wisconsin, 1974 (Economics); PhD Wisconsin, 1976 (Econometrics); History:
Cornell 1976-1982; Real world, 1982-1983; Return to ivory tower at Stern (then
GBA) NYU, 1983-2008; Toyota Motor Corp Professor, 2007-. Publications: Articles
see vita on home page; Books: Modeling Ordered Choices,
2010, Econometric Analysis, 7th Ed
(2011); Applied Choice Analysis (2006);
Software, NLOGIT (www.nlogit.com), Editor in Chief, Foundations and Trends in Econometrics. Editor in Chief, Journal of Productivity Analysis, Associate Editor, Journal of Economic Education, Journal of
Choice Modeling. Research interests: econometric methodology, discrete
choice modeling, efficiency and productivity analysis, health economics,
transportation, nonlinear estimation, entertainment and media.
Office: MEC 7-90, Ph. 998-0876, Fax. 995-4218
e-mail: wgreene@stern.nyu.edu
Home Page: http://www.stern.nyu.edu/~wgreene
Abstract
This course has two broad objectives: (1) This course will
provide students with an understanding of fundamental notions of data
presentation and analysis. We will develop tools to enable students to use
statistical thinking in the context of business problems. The course deals with
modern methods of data exploration (partly to reveal unusual or problematic
aspects of data sets), the uses and abuses of the basic techniques of
statistical inference, and the use of linear regression as a tool for
management and financial analysis. (2) There is randomness everywhere in life
and in the environment. We will develop models of probability and random
variables that help to understand the randomness of everyday life.
Prerequisites
I will assume that students are
familiar with routine algebra, exponents and logarithms as well as graphical
tools such as the slope and intercept of a straight line. Algebra will be used
freely throughout the course. We may have a rare occasion to use a derivative,
but the use of calculus will be sparing at most.
Course Requirements and
Course Grades
Final grades for the course will be
determined on the basis of the following components and weights:
* Mid-term
exam: 30%.
(Sample
midterm questions) (2007 Midterm Exam with
Solutions) (2008 Midterm Exam with Solutions)
* Final exam: 40%.
(Sample problems for study for the final)
(Notes
for sample problems) (2007 Final
Exam with Solutions) (2008 Final Exam with Solutions)
* In
class short (10 minutes) quizzes 10% (4 @ 2.5% ) 10% (in aggregate)
* Model development project (details below) 5%.
Students may work in groups of up to 5 and submit a single report for the
group..
* Homework assignments (details below): (6 @
2.5%) 15% (in aggregate). Students may work in groups of up to 3 and submit a
single report for the group.
Official policy at Stern mandates that grades in core
classes follow a distribution in which no more than 35% of students receive A
or A-.
Homework assignments are mandatory. Late submissions will
be accepted only with a persuasive justification, but not after the solutions
have been posted. All examinations are
open book, open notes, closed telephone, closed PDA, closed iPhone, closed
iPad, closed Droid, closed Crackberry, closed laptop. Do bring a hand
calculator to both exams. Links to copies of past exams appear below.
Honor Code: Of course.
Course Materials
* The
recommended text for this course is Basic Statistical Ideas
for Managers, 2nd edition by David Hildebrand, Lyman Ott and J. Brian Gray,
Thomson Learning, 2005. This book is available from the Professional Bookstore
on LaGuardia Place
and from online booksellers such as Amazon. Note that the text is not required.
We will not follow the text during the semester, and reading assignments in
the outline below are recommended for background only. You may find the text
useful for reinforcing the material we cover in class and as a source of many
examples and applications.
* Some
useful notes on several subjects. These include the bare bones
theoretical results and lots of examples. These are the Notes
indicated
below.
Essential statistical results for finance
Essential regression related results
for finance
Displaying data
Summary statistics
Basic probability
Combinations and permutations
Discrete random variables
The normal distribution
The basic linear regresssion model
Building
the regression model
Specification analysis of the regression
model
Variable selection
Forming confidence Intervals
Testing hypotheses
* The software for the course will
be Minitab, release 16. Version 16 is available from the Professional Bookstore
for about $100, but you can rent a copy for about $30 for the semester from
the website, http://www.e-academy.com
at the site, click on e-Store. (Introduction to Minitab) (How to use Minitab on the Stern Citrix Server)
Other Stuff
* Please
remember to turn off your cell phone before you come to class.
* Please
try to arrive early. Late entrances are disruptive.
* As a general rule, laptops are an annoyance
during class, particularly when you are checking your email, playing with
Facebook, tweeting or watching YouTube videos while others are studying
statistics. If you absolutely must use your laptop to take notes, please be respectful of the interests of your colleagues.
Course Outline
and Schedule)
Materials: (Introductory
notes for the course Notes 0: Introduction Right click to download
Session 1:
Introduction to statistics; data description and presentation; types of data;
Minitab.
Reading: Text:, Chapter 1 and
Sections 2.1-2.6, Notes on displaying data.
Materials: (Slides for this session Notes 1: Data
Presentation.)
Minitab Project Files: (Basic Statistics)
Session 2: Sampling, Descriptive statistics (mean, median,
mode, standard deviation), Covariance and correlation.
Reading: Text, Sections 2.2-2.6, Notes on
summary statistics.
Materials: (Slides for this session Notes 2:
Descriptive Statistics.) (An article about nonrandom sampling and
biased statistical analysis)
Minitab Project Files: (US Gasoline Market) (Basic Statistics)
Session 3: Probability, conditional and unconditional
probability, independence, joint probability, Bayes Theorem.
Reading: Text, Sections 3.1-3.3,
Notes on basic probability. Probabilities
and the Gulf oil spill.
Materials (Slides for this session Notes 3:
Probability.) (Sample Probability Exercises) (Some
famous and fun problems in probability)
Session 4: Probability and
expected value, applications of expected value.
Reading: Text, Sections 3.3, 4.3, Notes on basic probability.
Materials: (Slides for this session Notes 4: Expected
Value.) (Notes about credit
default swaps)
Session 5: Random Variables.
Reading:
Text, Section 4.1, 4.3, Notes on discrete random variables..
Materials: (Slides for this session Notes 5: Random
Variables.)
Session 6: Random variables,
covariance and correlation.
Reading: Text Section 4.4 4.5, Notes on discrete random
variables
Materials: (Slides
for this session Notes 6: Bivariate Random Variables.)
Session 7: Discrete
distributions, Bernoulli, binomial.
Reading: Text Section 5.1, 5.2, Notes on
discrete random variables, Notes on combinations and permutations.
Materials: (Slides
for this session Notes 7: Binomial and Poisson Distributions)
Session 8: Discrete
distributions, binomial, hypergeometric, Poisson.
Reading:
Text. Sections 5.2-5.3, Notes on discrete random variables, Notes
on combinations and permutations.
Materials:
(Slides for this session Notes 8: Binomial and Hypergeometric
Distributions)
(A science experiment to produce Poisson outcomes)
Session 9: The normal
distribution.
Readings:
Text Section 5.4, Notes on the normal distribution
Materials: (Slides for this session Notes 9: The
Normal Distribution.) (Sample Problems)
(What
is the margin of error?)
Session 10: Samples and sampling
distributions, normal distribution, large samples, law of large numbers,
central limit theorem.
Materials: Text Sections 5.5, 6.1 6.3, Notes on the normal
distribution
Materials: (Slides for this session Notes 10: The Central Limit Theorem and the
Law of Large Numbers.) (Random Walk Models for Stock Prices)
Minitab Project Files: (Cleared Calls)
(WHO Data)
(Basic Statistics)
Session 11: Central Limit
Theorem, normal approximations, lognormality, random walk.
Reading:
Text Sections 6.1 6.3, Notes on the normal distribution.
Materials: (Slides
for this session Notes 11: Normal Approximation and Random Walks.) (Seminar: That U.S. 37th Ranking by WHO) (Notes for seminar) (Lognormal Random Walks for Stock Prices)
Minitab Project Files: (WHO Data)
Session 12: Linear regression.
Reading:
Text Sections 11.1, 11.2, 11.4, Notes on the basic linear regression model.
Materials: (Slides for this session Notes 12: Linear
Regression.) (A
controversial regression study) (Slides
for an application of modeling) (Handout for
application) (Regression Analysis by WHO)
Minitab Project Files: (WHO Data)
(Drug Couriers)
(Trends
in Frequent Flyers) (US Gasoline Market)
Session 13: Linear regression
model, sample and population.
Reading:
Text Section 11.2, Notes on the basic linear regression model.
Materials: (Slides for
this session Notes 13: Regressions and Residuals.)
Minitab Project Files: (Movie Success) (US Gasoline Market)
Session 14: Least squares linear
regression, residual analysis, analysis of variance.
Reading:
Text Section 11.4, 11.5, Notes on the basic linear regression model.
Materials: (Slides for this session Notes 14:
Regression Analysis.)
Minitab Project Files: (Movie Success)
(WHO
Data)
Session 15: Prediction,
elasticity, functional form, correlation and covariation.
Reading:
Text Section 11.5, Notes on the basic linear regression model.
Materials: (Slides
for this session Notes 15: Regression and Correlation.)
Minitab Project Files: (Sale Prices for Monet
Paintings) (Trends in Frequent Flyers) (Cost Data for
U.S. Utilities) (Salaries) (US Gasoline Market)
Session 16: Prediction, regression to the mean,
measurement error, truncation, selection.
Reading: Notes on the basic linear
regression model.
Materials: (Slides for this session Notes 16: Specifying the Regression Model.)
Minita Project Files: (US Gasoline Market)
(Sale Prices for
Monet Paintings)
Session 17: Multiple regression,
Reading: Text Chapter 12, Notes on building the regression model
Materials: (Slides
for this session Notes 17: Multiple Regression Part 1) (Multiple
Regression with Minitab)
Minitab Project Files: (US Gasoline Market)
(Sale Prices for
Monet Paintings)
Session 18: Multiple regression
Reading:
Text Chapter 12, Notes on building the regression model.
Materials: (Slides
for this session Notes 18: Multiple Regression Part 2)
Minitab Project Files: (US Gasoline Market)
(Sale Prices for
Monet Paintings) (WHO Data) (Computer Prices)
(UK Electronics Stores)
(House Prices)
Session 19: Multiple regression,
Reading: Text Chapter 12, 13, Notes on specification analysis of the
regression model.
Materials: (Slides
for this session Notes 19: Multiple Regression Part 3)
Minitab Project Files: (US Gasoline Market)
(WHO Data)
(Profits and R&D Data) (Movie Madness)
Session 20: Multiple regression
Reading: Text Chapter 12, 13, Notes on specification
analysis of the regression model.
Materials: (Slides
for this session Notes 20 Multiple Regression Part 4)
Minitab Project Files: (WHO Data)
(Sale Prices for
Monet Paintings) (Longley
Data)
Session 21: Statistical
inference, confidence intervals
Reading: Text Chapter 7, 8, 9,
Notes on forming confidence intervals.
Materials: (Slides for this session Notes 21:
Statistical Inference.)
Minitab Project Files: (Credit Application data)
Session 22: Statistical tests
Reading: Text Chapter 8, 9, Notes on testing hypotheses.
Materials: (Slides for this session Notes 22: Testing
Hypotheses Part 1)
Minitab Project Files: (Credit Application data)
Session 23: Statistical tests
Reading: Text Chapter 8, 9, Notes on testing hypotheses.
Materials: (Slides for this session Notes 23; Testing
Hypotheses Part 2.)
Minitab Project Files: (Credit Application data) (Econometrics Midterm Grades data)
Session 24: Hypothesis tests,
Reading: Text Chapter 8, 9, 11, Notes on testing hypotheses.
Materials: (Slides for this session Notes 24:
Hypothesis Testing) (Notes on the Power of a Test) (Tests
about Variances)
Minitab Project Files: (Credit Application data) (US Gasoline Market)
(Sale Prices for
Monet Paintings) (German Health
Survey Data) ( Sydney/Melbourne
Travel Choice)
Session 25: Analyzing Qualitative Data
Reading: None
Materials: (Slides
for this session Notes 25: Analyzing Qualitative Data.) (A
Study about Obesity) (The Netflix Prize)
Minitab Project Files: (Credit Application data) (German Health Survey Data) ( Sydney/Melbourne Travel Choice)
Individual Problem Sets and Assignments
Students may work with one of their colleagues on these homework
assignments and submit your assignment as a group. (Groups may have no more
than two students.). All data sets for the assignments are linked below.
You can left click to open them in Minitab, or right click to download them to
your own computer.
Assignment 1. Data
Description and basic probability. Problem set 1 Problem
set 1 solutions
Data sets: (HOG-Ex0201.mpj) (HOG-Ex0202.mpj)
(HOG-Ex0218.mpj)
(HOG-Ex0222.mpj)
(97employ.mpj)
(WHO-HealthStudy.mpj)
Assignment 2. Probability. Problem
set 2 Problem
set 2 solutions
Assignment 3. Probability
and Random Variables, Expected Value, Poisson and Hypergeometric Distributons,
Normal Distribution. Problem set 3 Problem
set 3 solutions
Data sets: (WHO-HealthStudy.mpj) (Easton.mpj) (salary.mpj) (Movies9OCT2003.mpj)
Assignment 4. Basic
Regression. Problem set 4 Problem set 4 solutions
Data sets: (WHO-HealthStudy.mpj) (EconGrades.mpj) (heating.mpj) (KansasCtyPopn.mpj)
Assignment 5. Multiple
Regression. Problem set 5 Problem set 5 solutions
Data sets: (UKElectronics.mpj) (GermanHealth.mpj) (MoreMoviemadness data.mpj)
(Credit Application data)
Assignment 6. Statistical
Inference. Problem set 6 Problem set 6 solutions
Data sets: (German
Health Survey Data) (Sale Prices for Monet Paintings)
Model
Development Project
Notes
on the model development project Data for Model Development (Minitab) (Excel)