BAD 260: Intro Statistical Methods

Dominican University

Spring 2006

 

Instructor: Dr. Carol Tallarico

Office: FA 111

Office Phone: 708-524-6478

Cell Phone: 708-351-8961

Email: ctalla@dom.edu

Office Hours:  Tuesday and Thursday 2-3pm and by appointment

 

Course Description: An introduction to basic concepts and procedures including measures of central tendency and variability, probability, sampling distributions, hypothesis testing, correlation and regression, and nonparametric measures.

 

Expected learning outcomes:

By the end of the semester successful students should

  1. Be able to calculate and interpret measures of central tendency and variation.
  2. Be able to critically develop hypotheses for testing and determine the appropriate data sample to use.
  3. Be able to conduct regression analysis and forecasting using computer software packages.
  4. Be able to explain the difference between Type I and Type II error in hypothesis testing.
  5. Be able to develop and analyze probability distributions (Binomial, Poisson, and Normal).

 

Pre-requisites: Computer Information Systems 206

                          Mathematics 170               

 

Meeting Times: BAD 260-01 meets Tuesday and Thursday 5-6:15pm in Lewis 131

 

Instructional Method: This course will be taught primarily through lecture, with some in class computing and problem solving.

 

Texts:

Required Texts:

Anderson, D., Sweeney, D., and Williams, T.  Statistics for Business and Economics.  9th edition. Mason, Ohio: Thomson-Southwestern Publishers, 2004. (ISBN 0-324-20082-X)

 

Additional Materials:

Work Book (ISBN: 0-324-20086-2)

Microsoft Excel Companion (ISBN: 0-324-22253-9)

Both of these resources are available at www.swlearning.com

 

You will also need a calculator for this course.  A cell phone is not a calculator.

Assessment of Student Learning:

Grading Scale:

90-100      A

80-89        B

70-79        C

60-69        D

0-59          F

 

Assignment

Possible Points

Your Score

Homework Ch. 1

1

 

Homework Ch. 2

1

 

Homework Ch. 3

1

 

Homework Ch. 4

1

 

Homework Ch. 5

1

 

Homework Ch. 6

1

 

Homework Ch.7

1

 

Homework Ch. 8

1

 

Homework Ch. 9

1

 

Homework Ch.14

1

 

Homework Ch.15

1

 

In Class Problem #1

1

 

In Class Problem #2

1

 

In Class Problem #3

1

 

In Class Problem #4

1

 

Test 1 (Ch 1, 2, 3)

15

 

Test 2 (Ch 4, 5)

15

 

Test 3 (Ch 6, 7)

15

 

Test 4 (Ch 8, 9)

15

 

Final Project (Ch 14, 15)

15

 

Final Presentations

10

 

Total

100

 

 

Tests:

Each test will cover the chapters listed.  The test problems will be very similar to the homework problems. If there is a conflict for a test or presentation it can be made up only with prior permission from the instructor.  Missed presentations or tests can only be made up if an acceptable excuse is provided with appropriate evidence (doctor’s note, jury duty payment stub, etc…).  Otherwise, they will be recorded as zeroes. Please note there are very few excuses I will consider acceptable after an exam has passed.  In almost all cases, I expect you to discuss the absence with me before the test or presentation.

 

In Class Problems:

Four times throughout the semester I will randomly 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.

Homework:

Homework is assigned for every chapter.  Each completed chapter homework will be worth 1% of your grade.  Late homework will be worth half credit.  Due dates are listed in the course calendar.  I will try to remind you of them as the semester moves forward, however, they are due on the dates listed unless specifically stated otherwise.

 

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:  (Start collecting data EARLY)

1.  Determine a research question you would like your study to answer.  The study should be specific to your interests and the data you might have available to 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.

 

In your regression you will need 1 dependent variable, 3-5 independent variables, and at least 30 observations.

 

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:               Information above needed for at least 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:              Information above needed for at least 30 students

                       

There will be many possible research questions that may appeal to you.  Of primary importance is whether the data for your specific research question is available or not.  So you definitely want to consider the problem of actually obtaining the data which will be the next part of your project. 

 

3.  Input your data into Excel.  Your variables are the column headings and the observations are the row headings.

 

4.  Run your regression in Excel.

 

5.  Write up your results.

 

  1. Discussion of Theory / Literature (Why are you doing this?  Who cares?)
  2. Discussion of Model (Why are you using the variables you selected?)
  3. Discussion of Hypotheses (What do you think the results will be and why?)
  4. Data (Explain the data. Where did you get it? How is it measured?)
  5. Results
    1. Coefficients (Sign and Magnitude)
    2. T-scores (Significance)
    3. Interpretation of coefficients.  (What do they mean? Support hypotheses?)
    4. F-Test (Is regression good?)
    5. R-squared or Adjusted R-squared (Is regression good?)
    6. Analysis of Residuals (Normality, Autocorrelation, Heteroskedasticity)
    7. Multicollinearity (Correlation Matrix)
  6.  Conclusions

 

Turn in Data in ExcelSheet and Write-Up.  Your write up should be at least 3 pages long and should be completely self-explanatory.  Assume that your reader has no knowledge of regression analysis.

 

Note: This is research.  There is no “right answer”.  I want to know why your model is good, what your results are telling, and why what you have found is important.  Remember, anyone can put data into Excel and pop out some results, what makes you a statistician is that you can interpret the results and give them meaning.

 

Final Project Presentation:

You will do a presentation explaining your project and your results.  Simply go through the steps above as your presentation.  Your presentation should be 5 minutes long.

 

Note:  I will be grading the projects for content, but the presentations will be graded primarily by your peers for quality of presentation.

 

Attendance Policy:

Attendance is not specifically required for this course.  However, you are expected to attend class and 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 would like to meet with me outside of class for further clarification on course material, I would be more than happy to schedule an appointment with you.  If, however, you choose not to attend class, I will not make myself available to teach you the information already covered in class.

 

 

Course Calendar:

Week

Day

Date

Assignments Due

Lecture Chapters

1

Thurs

Jan 12

 

Ch 1, 2

 

Tues

Jan 17

HW Ch 1

Ch 2

2

Thurs

Jan 19

HW Ch 2

Ch 3

 

Tues

Jan 24

 

Ch 3

3

Thurs

Jan 26

HW Ch 3

Review

 

Tues

Jan 31

Test 1

 

4

Thurs

Feb 2

 

Ch 4

 

Tues

Jan 7

 

Ch 4, 5

5

Thurs

Feb 9

Ch 4 HW

Ch 5

 

Tues

Feb 14

 

Ch 5

6

Thurs

Feb 16

Ch 5 HW

Review

 

Tues

Feb 21

Test 2

 

7

Thurs

Feb 23

 

Ch 6

 

Tues

Feb 28

 

Ch 6,7

8

Thurs

Mar 2

Ch 6 HW

Ch 7

 

Tues

Mar 14

 

Ch 7

9

Thurs

Mar 16

Ch 7 HW

Review

 

Tues

Mar 21

Test 3

 

10

Thurs

Mar 23

 

Ch 8

 

Tues

Mar 28

 

Ch 8,9

11

Thurs

Mar 30

Ch 8 HW

Ch 9

 

Tues

Apr 4

 

Ch 9

12

Thurs

Apr 6

Ch 9 HW

Review

 

Tues

Apr11

Test 4

 

13

Tues

Apr 18

 

Ch 14

 

Thurs

Apr 20

 

Ch 14, 15

14

Tues

Apr 25

 

Ch 15

 

Thurs

Apr 27

Ch 14, 15 HW

Work on Projects

Final

Thurs

May 4

3:30- 5:30pm

Final Projects

Presentations

 

 

Let’s have a good semester, and feel free to talk to me about any problems or concerns you have with the class or anything else.

 

I want all of you to succeed, but if I don’t know if you’re having trouble it is very difficult for me to help you. 

 

GOOD LUCK!!!

 

CT