**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

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: http://www.kalamazoostatistics.blogspot.com/**

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 2004, Research Projects Winter 2005, Research Projects Spring 2006

Forecasting with
regression (powerpoint) Spring 2003

Forecasting problems (powerpoint)
Spring 2003

**links**

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:

The Gallup Poll

Federal Statistics (from 70 agencies)

U.S.
Microeconomic datasets from

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 (

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)

Department of Economics homepage

Questions, problems, or comments?

email: cstull@kzoo.edu