Business Statistics
Economics 160

Chuck Stull

Winter 2013

Course Description

Business Statistics introduces statistical methods used in economics and business.  It is not a continuation of econ 155 (Mathematical Methods), but instead a course on statistics, probability, estimation, empirical studies, and related issues.  The class will emphasize quantitative reasoning over proofs and mathematical formalism.  We will work extensively with numbers (bring a calculator to class) and we will use Microsoft Excel for some assignments.  A good understanding of statistical analysis is crucial to understand how the world works.

I.    Summary Statistics

    Descriptive statistics are the most familiar part of statistical analysis.  Averages-- mean, median, mode-- sometimes called measures of central tendency; percentages; and measures of spread or dispersion -- variance, standard deviation-- are very common.  We won't belabor these because most students have had some exposure to them.  For an explanation of the basics, see Statistics Every Writer Should Know.

    While calculations for Samples and Populations look similar, it's important to remember that a sample is only an estimate and we'll need to use probability theory to relate it to the true population parameter.

    Graphs are often a useful supplement to descriptive statistics.

    We can also look at relations between variables, using tools like correlation, covariance, and regression

II.  Collecting Data

    Whenever you read a statistic, a question should pop into your mind, "where did this number come from?"  Not all numbers are created equally, even if they are printed on really nice glossy paper.  Methodology and definition are truly important.
    Samples need to be random.  Getting a true random sample requires real effort.  Try these online sources of random numbers  or these random numbers.
    Lots of good data is available online.  See Finding Data on the Internet for links to some sources.
Online data collection still has many issues to resolve. 

III. Uncertainty, Chance, and Probability

    Gambling involves uncertain outcomes and gambling was a primary motivation for early studies of probability.  Games of chance are easily analyzed using classical probability methods.  Casino gambling and online gambling are both growing industries, despite the fact that the games they offer are not fair bets.  (The expected value of the bet is negative; if you play long enough you will lose all of your money.)
    This edge to the house makes possible the gambling palaces of Las Vegas, as well as good returns to the stockholders.  (See the investor relations pages from Harrah's, for example.)  Sophisticated use of probability theory with enough data can give the gambler an edge: see  Hacking Las Vegas (Wired magazine) about a group of college students who beat the casinos.
A number of websites offer basic analysis of casino games but they are so gimmicked with pop-up windows and promotions that I'm reluctant to include them here.  Try Yahoo's gambling section for a selection.  Wizards of Odds is interesting. 

    Life expectancy is an another interesting example of expected value.  Since expected values are based on probabilities, we can improve these calculations with more information.  In other words, we can use conditional probabilities to more accurately estimate lifespans.  Northwestern Mutual Insurance has a fun website:  try their Longevity Game .

    Financial markets also display large amounts of uncertainty.  The theory of efficient markets concludes that changes in stock prices should be random and unpredictable.  This means returns from a portfolio will be more uncertain than a salesman at a brokerage would like you to believe.

IV. Analyzing data

    Numbers by themselves are boring.  They only have interest in the context of a research question.  Hypothesis testing is a statistical way to answer questions like, "does Energizer last longer? " or "do tax cuts increase consumer spending?"  For some questions we can compare populations.  For other questions we want to relate variables using correlation or regression .  Sometimes we will look for trends and cycles using time series analysis .

V. What else can we do?

Regressions are frequently used for forecasting.  Many economic variables are interconnected, so forecasters build systems of equations to make forecasts. Fairmodel is a large macromodel (US or international) available for free, online.

Tests and projects

We will have two midterms and a final-- each with a variety of types of questions.  The material on each exam will be discussed in class.

Exam I        Wednesday, January 30             7:00 pm (4th week)

Exam II       Monday, February 25                 7:00 pm (8th week)

Exam III      Tuesday, March 19                     1:30 pm (Final exam week)

Topics from last time are still available online.  These will be updated before each test.
Topics for Exam One

Topics for Exam Two

Topics for Final Exam


Class Blog page:


We'll also have assignments, quizzes, and problem sets throughout the term.

Throughout the quarter we work on original research projects .  I'm including links to previous class projects below.  (please note: these are web summaries of the project-- the online version may be missing elements included in the original paper.) These classes had different assignments, as well.  Studies Winter 2002 , Stats Research Spring 2002 , Stats Projects Fall 2002 , Statistical Projects Spring 2003 , Research Projects Winter 2004, Research Projects Spring 2004Research Projects Winter 2005, Research Projects Spring 2006


Forecasting with regression (powerpoint) Spring 2003
Forecasting problems (powerpoint) Spring 2003


A textbook website:
The Practice of Business Statistics by David Moore, George McCabe, William Duckworth, Stanley Sclove; W.H. Freeman and Company, 2003.  The website has an unusual amount of useful material including statistical animations .

a few statistical sources:


US Census Bureau

The Gallup Poll

Federal Statistics (from 70 agencies)

Federal Reserve Economic Data

U.S. Microeconomic datasets from Macalester College's Department of Economics

EconDash discusses economic measurement and data

A website to accompany Statistics: Concepts and Controversies has some fun java applets illustrating various statistical concepts.

John Allen Paulos (Math Department Temple University) writes engaging stories (and books) on statistics and mathematical literacy

Statistical Resources on the Web: Business and Industry (U of M)

The Data and Story Library

The Journal of Statistics Education .

Life Expectancy Among 19th Century Baseball Players


Data for Problem Set #1 Fall 2002 (Excel file)

Data for Problem Set #2 Fall 2002 (Excel file)


Chuck Stull's homepage

Department of Economics homepage

Kalamazoo College Homepage


Questions, problems, or comments?