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Gallery Fall 2002
Econ 150 Economic Statistics
Here are the project assignments, ideas, and links to a gallery of successful projects. These projects were created in the fall, 2001. Econ 150 is a first course in statistics in economics. We use a spreadsheet intensive method of learning statistics. Our textbook is e.stat for business and economics (Southwestern College Publishing).
Computer Projects
The project descriptions given here are those from Fall, 2001. Assignments may
change from term to term.
There are five computer projects to be completed and graded. With each project, make a presentation in Excel describing the project and its conclusions. Each project should demonstrate use of the concepts developed in the course. Creative choice of topic will yield a better grade. The quality of presentation matters. Your submission should be authoritative. Submit an Excel file electronically as an e-mail attachment or by dropping a file in the course folder.
Each team may plan projects jointly and gather data together. Each member of
the team should use his or her own variables and prepare his or her own report
for Projects B, D, and E. Before submitting the completed project to the recitation
leader, please have your teammates complete a critique to be handed in with
the project. The critique may be submitted by electronic mail. The form given
below is suggestive. See the Prometheus site for a gallery of old projects.
Project A Descriptive Statistics
for team grade
Develop some original data (not published). Make a relative frequency polygon
and compute descriptive statistics for at least two random variables with 15
or more observations for each. Make clear the method used to generate the sample
used.
Examples: Go to two car dealerships and collect data on the sticker prices of
cars.
Use the Internet to find prices of comparable products from two sources.
Survey students (not from Tennessee) about their monthly expenditures for long
distance telephone. Compare men and women or first year and fourth year.
Gallery A
Hurter & White: Pottie Practices
Lloyd & Hall: Faculty Publishing
Schacht & Proffitt: Clean Sheets
Singhal & Rao: Alcohol Tolerance
Project B: Probability Exploration
individual
Use one of the PDFs in chapter 7 to explore a phenomenon you observe. Make observations, plot a relative frequency polygon, and compute descriptive statistics to define values for needed parameters of the PDF. Use Excel to generate a table of probabilities for the phenomenon and plot them. Then, write a description of the phenomena and, using probability statements, make forecasts about the phenomenon. Observing something that might be a Poisson process works best.
Examples: Observe how many people enter the Bookstore during each minute at
some interval in the day. Use the Poisson distribution with the appropriate
lambda you have estimated to define a specific probability distribution for
entrance per minute. Create a table of probability values for that PDF. Make
probability statements about entrance per minute at the Bookstore assuming that
your parameter values are correct.
Ask students as they leave the library (with permission) whether they used library
materials on that visit or just studied using their own materials. Treat the
proportion studying as the parameter of a binomial PDF, compute probability
values for the specific binomial. One must observe several (a dozen) blocks
of visitors to establish a proportion studying in each block. The proportion
in a block is an observation. Use the binomial to make probability statements
about the likelihood that a particular number of library visitors from a random
group of a given size will be studiers rather than researchers
Gallery B
Project C Monte Carlo Study
team
For Project C, each team will prepare a single report and submit it jointly for a common grade.
This project asks that you simulate data along the lines discussed in chapter
11. The chapter contains a number of populations of 1,000 numbers. Pick one
of these or create your own. Treat the population as though it were unknown.
You are to explore how the mean of sample means relates to the population. You
may compare the distribution of the sample mean to the distribution of other
sample statistics.
Draw M, say 100, random samples of size n (say 30) from the population using
the procedure described in chapter 11. Compute the value of the sample mean
for each sample. Finally, observe the pattern of the sample mean over the several
samples from the same population. What is the mean of the distribution of the
sample means? What is the standard deviation of the sample mean over the several
samples? What mean and standard deviation do you expect? Plot a frequency polygon
for the sample means. Repeat the whole process with a different sample size,
say, a much larger sample. How does the distribution of the sample mean differ?
Compare your results for the sample mean with that predicted by the Central
Limit Theorem. Your summary paragraphs interpreting your simulation exercise
are important.
The descriptive above makes a basic project. Chapter 8 presents a variety of
ideas for deeper exploration.
Gallery C
Project D Hypothesis Tests
individual
Using data on two random variables, compute hypothesis tests at the 5 percent
level for differences in means. You may use published data. You may find data
on the World Wide Web. If you use a survey, carefully define the population,
sample frame, and sampling method.
Examples: Compare the expected salaries in ten years of male and female students.
Compare the height of daughters with the height of their mothers.
Do Munchi Mart prices differ from those at Compton's or Hill's?
Has the mean rate of growth of per capita GDP been higher in coastal versus
landlocked countries?
Gallery D
Project E Regression (counts double) individual
Investigate the relationship between two or more random variables using regression.
Use original or published data about variables that you expect to be related
to one another. Formulate and test appropriate hypotheses. Plot a scatter diagram
and discuss the pattern of the residuals. (Using observations over time raises
more difficult statistical issues and so make less effective Project Es.)
Examples:
Observe the price of individual breakfast cereals at a supermarket, along with
calories and other nutrients and weight. Regress price per ounce on attributes
of the cereal.
Use a guide to colleges and regress tuition on attributes of colleges.
Consult a computer magazine and regress price on speed, RAM, disk size, etc.
Survey students and regress GPA on SAT, high school GPA, and use a zero-one
variable for major.
Survey students and regress number of dates this month on height and GPA separately
for men and women.
Regress Presidential vote ratio state by state on the unemployment rate, per
capita income, and other attributes of the states.
Gallery E