ECON 260-02: Statistics for Business and
Economics
Semester: Spring 2009 Class Meetings:
Wednesday, 7pm-10pm
Instructor:
Kathleen Odell Room: TBA
Office:
708/524-6997, x 1312 Office
Hours: Before class & by appointment
email: kodell@dom.edu Course web page: http://blackboard.dom.edu
Course
Description:
This course will provide an introduction to basic
statistical concepts and procedures including data collection and presentation,
measures of central tendency and variability, probability, sampling
distributions, confidence intervals, hypothesis testing, and linear
regression.
Expected
learning outcomes:
By the end of the semester successful
students should:
1.
Be
able to meaningfully display a data set both graphically and in tables;
2.
Be
able to calculate and interpret measures of central tendency and variation;
3.
Be
able to calculate the probability of an event;
4.
Be
familiar with discrete and continuous probability distributions;
5.
Be
familiar with sampling and sampling distributions;
6.
Be
able to estimate confidence intervals for a predicted value;
7.
Be
able to set up and conduct hypothesis testing; and
8.
Be
able to design and execute a simple linear regression.
Prerequisites:
Computer Information Systems 120 and Mathematics
170 (Recommended). Please see the instructor if you do not have the recommended
prerequisite courses.
Instructional
Method:
This course will be a combination of lecture
and in-class problem solving using statistical software.
Required
Text:
Anderson, D., Sweeney, D., and Williams,
T. Statistics
for Business and Economics. Revised
10th edition.
Additional
Materials:
Calculator (for in-class problems and
exams).
Evaluation
Criteria:
There are 1000 points possible over the
course of the semester, distributed as follows:
Homework – 100 points (10 points each, best
10 will be counted)
In Class Problems – 50 points (10 points
each)
Exams – 600 points (4 exams; 150 points each)
Final Project – 250 points (150 points for
report; 100 points for presentation)
The anticipated grading scale will be 900-100
(A), 800-899 (B), 700-799 (C), 600-699 (D), <600 (Fail).
Homework:
Homework is assigned for every chapter. Each
homework will be worth 1% of your grade.
Late homework will be worth half credit.
Due dates are listed in the course calendar. Problem sets are due on the dates listed
unless specifically stated otherwise. Stapled hard copies of homework must be
turned in during class on the due date.
I cannot accept homework problem sets by email. Solutions to the homework problems will be
provided when problem sets are returned.
In Class Problems:
At five unannounced times
throughout the semester I will collect the problems we work on in class. Each of these collections will be worth 1% of
your grade. They cannot be made up nor
will they be accepted late. This is the
attendance / class participation portion of your grade.
Tests:
There will be four
tests over the course of the semester. Each
test will cover the chapters listed in the course schedule. The test problems will be very similar to the
homework problems.
Final Project:
You will complete a
final project using linear regression. A
full description of the project is given later in this syllabus. Your project will include a written report
(worth 150 points) and an in-class presentation (100 points).
Attendance
Policy:
Attendance is not specifically required for
this course, but you should plan to attend class. You are responsible for all material that is
covered in class that might not be included in the book or may be slightly
different from what is included in the book.
If you miss class, you should get the notes from another student. I cannot provide missed notes (although I
will make any electronic notes available on Blackboard). Also note that if you miss a day when I
collect the in-class problems, this will affect your grade. Homework is due almost every week, so you need
to come to class to turn in your homework.
If you miss an exam (or your final
presentation), you will get a zero. Make
up exams will not be available unless the student can provide, in advance,
written documentation of accident, illness, approved University event, or
family emergency. Traffic, misreading the syllabus, sleeping through class, and
similar explanations are not valid reasons for missing an exam, and a makeup
exam will not be provided under these circumstances. You must make arrangements IN ADVANCE if you
know you will miss an exam – after the fact there is almost no situation in
which I will be able to let you make up the exam.
Chapter
Coverage and Homework Problems:
(Subject
to change!!!)
|
Section |
Title |
Problems |
|
1-1 |
Applications
in Business & Econ |
|
|
1-2 |
Data |
|
|
1-3 |
Data
Sources |
|
|
1-4 |
Descriptive
Statistics |
|
|
1-5 |
Statistical
Inference |
|
|
1-6 |
Computer
& Statistical Analysis |
|
|
Exercises |
|
6,
8, 12, 14, 19 |
|
Section |
Title |
Problems |
|
2-1 |
Summarizing
Qualitative Data |
2,
6 |
|
2-2 |
Summarizing
Quantitative Data |
11,
12, 18 |
|
2-3 |
Exploratory
Data Analysis: S & L |
22,
27 |
|
2-4 |
Cross
Tabulations & Scatters |
36,
37 (c,d) |
|
Section |
Title |
Problems |
|
3-1 |
Measures
of Location |
3,
4, 7 |
|
3-2 |
Measures
of Variability |
15,
18 |
|
3-3 |
Measures
of Shape, Location, Outliers |
27(a,b),
28(a,b), 30, 31, 32 |
|
3-4 |
Exploratory
Data Analysis |
|
|
3-5 |
Measures
of Association Between Variables |
45,
48, 49 |
|
3-6 |
Weighted
Mean and Grouped Data |
|
|
Section |
Title |
Problems |
|
4-1 |
Experiments,
Counting Rules, Probabilities |
9,
10, 11, 12 |
|
4-2 |
Events
and their Probabilities |
16,
18, 21 |
|
4-3 |
Basic
relationships of Probability |
27,
28, 29 |
|
4-4 |
Conditional
Probability |
33,
34 |
|
4-5 |
Bayes’
Theorem |
|
|
Section |
Title |
Problems |
|
5-1 |
Random
Variables |
6 |
|
5-2 |
Discrete
Probability Distributions |
7,
12 |
|
5-3 |
Expected
Value and Variance |
15,
17, 20 |
|
5-4 |
Binomial
Probability Distribution |
26,
33, 34, 35 |
|
5-5 |
Poisson
Probability Distribution |
38,
43, 44 |
|
5-6 |
Hypergeometric
Probability Distribution |
|
|
Section |
Title |
Problems |
|
6-1 |
Uniform
Probability Distribution |
2,
5, 6 |
|
6-2 |
Normal
Probability Distribution |
12,
14, 17, 21, 22, 25 |
|
6-3 |
Normal
Approximation of Binomial Probabilities |
|
|
6-4 |
Exponential
Probability Distribution |
|
|
Section |
Title |
Problems |
|
7-1 |
Sampling
Problem |
|
|
7-2 |
Simple
Random Sampling |
|
|
7-3 |
Point
Estimation |
11,
17 |
|
7-4 |
Intro
to Sampling Distributions |
|
|
7-5 |
Sampling
Distribution of x-bar |
19,
23, 28 |
|
7-6 |
Sampling
Distribution of p-bar |
32,
35, 40 |
|
7-7 |
Properties
of Point Estimators |
|
|
7-8 |
Other
Sampling Methods |
|
|
Section |
Title |
Problems |
|
8-1 |
Interval
Est. of µ, σ known |
6,
10 |
|
8-2 |
Interval
Est. of µ, σ unknown |
15,
18, 19 |
|
8-3 |
Determining
Sample Size |
23,
27, 28 |
|
8-4 |
Interval
Est. of Population Proportion |
32,
33, 36, 38 |
|
Section |
Title |
Problems |
|
9-1 |
Developing
Null and Alternative Hypotheses |
1,
2, 3, 4 |
|
9-2 |
Type
I and Type II Errors |
5 |
|
9-3 |
Hypothesis
Test for µ, σ known |
16,
18, 20 |
|
9-4 |
Hypothesis
Test for µ, σ unknown |
28,
33 |
|
9-5 |
Hypothesis
Test of Pop. Proportion |
39,
41, 44 |
|
9-6 |
Hypothesis
Testing and Decision Making |
|
|
9-7 |
Calculating
the Probability of Type II Error |
|
|
9-8 |
Determining
the Sample Size |
|
|
Section |
Title |
Problems |
|
14-1 |
Simple
Linear Regression Model |
|
|
14-2 |
Least
Squares Method |
1,
6, 7, 11, 12 |
|
14-3 |
Coefficient
of Determination |
18 |
|
14-4 |
Model
Assumptions |
|
|
14-5 |
Testing
for Significance |
27 |
|
14-6 |
Using
the Regression for Estimation |
36 |
|
14-7 |
Computer
Solution |
43 |
|
14-8 |
Residual
Analysis (Model Assumptions) |
49 |
|
14-9 |
Residual
Analysis (Outliers) |
52 |
|
Section |
Title |
Problems |
|
15-1 |
Multiple
Regression Model |
|
|
15-2 |
Least
Squares Method |
5,
6, 9 |
|
15-3 |
Multiple
Coefficient of Determination |
11,
16 |
|
15-4 |
Model
Assumptions |
|
|
15-5 |
Testing
for Significance |
25 |
|
15-6 |
Using
the Regression for Estimation |
30 |
|
15-7 |
Qualitative
Independent Variables |
35,
37, 38 |
|
15-8 |
Residual
Analysis |
|
|
15-9 |
Logistic
Regression |
|
Final Regression
Project:
1. Determine a research question that you would
like to answer. Choose a question that
interests you.
For
regression studies you are looking for a cause and effect type question.
Examples: What
determines the number of games baseball teams win?
What determines MBA
GPAs?
What determines loan
defaults?
2. Determine the data you will use to answer
this question.
Your
regression will require 1 dependent variable, 3-5 independent variables, and at
least 30 observations. (More is
better!)
Examples: What determines the number of games
baseball teams win?
Dependent Variable:
Number of games won
Independent Variables: Team
ERA
Team
Batting Average
Team
Fielding Percentage
30 observations: 30
teams
What determines MBA
GPAs?
Dependent Variable:
MBA student GPA
Independent Variables: Student
GRE score
Student
undergraduate GPA
Number of years since
completing undergrad
30 observations: 30 students
You
will probably be able to think of many interesting research questions, but you
must consider whether the data you need is available. Consider the problem of actually obtaining
the data you need. If you can’t get the
data, you can’t do the project.
3. Enter
your data into Excel.
4. Run
your regression in Excel.
5.
Write up your results.
Turn in a printed copy of your Excel file
along with your report. Your report
should be at least 3 pages long and should be completely self-explanatory.
Note: This is research. There is no “right
answer.” Explain why your model is good,
how you interpret your results, and why what you have found is important. Remember,
anyone can put data into Excel and push
the regression button; what makes you a statistician is that you can interpret the results and give
them meaning.
Final
Project Presentation:
During finals week, you will make a presentation
explaining your project and your results.
Note:
I will be grading your written report for content, but the presentations
will be graded primarily by your peers for quality of presentation.
Late
projects and presentations will not be accepted.
Course Calendar:
|
Week |
Day |
Date |
Assignments Due |
Lecture Chapters |
|
1 |
W |
Jan 21 |
|
1, 2 |
|
|
|
|
|
|
|
2 |
W |
Jan 28 |
Chapter 1 |
2 (continued), 3 |
|
|
|
|
|
|
|
3 |
W |
Feb 4 |
Chapter 2 |
3 |
|
|
|
|
|
|
|
4 |
W |
Feb 11 |
Chapter 3 TEST 1 |
4 |
|
|
|
|
|
|
|
5 |
W |
Feb 18 |
|
4, 5 |
|
|
|
|
|
|
|
6 |
W |
Feb 25 |
Chapter 4 |
5, 6 |
|
|
|
|
|
|
|
7 |
W |
Mar 4 |
Chapter 5 TEST 2 |
6 |
|
|
|
|
|
|
|
8 |
W |
Mar 11 |
NO CLASS |
Mid-Semester Vacation |
|
|
|
|
|
|
|
9 |
W |
Mar 18 |
Chapter 6 |
7 |
|
|
|
|
|
|
|
10 |
W |
Mar 25 |
Chapter 7 TEST 3 |
8 |
|
|
|
|
|
|
|
11 |
W |
April 1 |
Chapter 8 |
9 |
|
|
|
|
|
|
|
12 |
W |
April 8 |
|
9, 14 |
|
|
|
|
|
|
|
13 |
W |
April 15 |
Chapter 9 TEST 4 |
14, 15 |
|
|
|
|
|
|
|
14 |
W |
April 22 |
Chapter 14 |
15 |
|
|
|
|
|
|
|
15 |
W |
April 29 |
Chapter 15 (end of
class) |
LAST DAY OF CLASS Work on Projects |
|
|
|
|
|
|
|
Final |
|
TBA |
Final Project
Presentations |
|
This
schedule is subject to change at the instructor’s discretion.