1
BUS 500, “Statistical and Quantitative Analysis”
Syllabus–Fall 2009
INSTRUCTOR: /Todd Easton
/ OFFICE HOURS:OFFICE: / Franz 312 / MONDAY: / 2:30 – 3:30 pm
OFFICE PHONE: / 503-943-7209 / WEDNESDAY: / 9:30 – 11:00 am
HOME PHONE: / 503-234-2453 / 2:30 – 3:30 pm
FAX: / 503-943-8041 / 7:00 – 7:30 pm
EMAIL: / / THURSDAY: / 9:00 – 10:00 am
CLASS WEBSITE: / teaching.up.edu/BUS500 / FRIDAY: / 9:00 – 10:00 am
ROOM & TIME: / Buckley Center 15
Wed., 4:10 pm - 6:55 pm / OR by appointment!
Course Prerequisite: none
Course Description: Covers the statistical and quantitative tools for conducting basic research in the business environment. Topics include descriptive statistics, probability distributions, hypothesis testing, extensive treatment of multiple regression models. Microsoft Excel is the tool of analysis for the course.
Learning Goals Addressed in this Course:
Research SkillsStudents will carry out a research project using primary data. Each will consult with the professor about a suitable research questions and data to answer it.
Analytic and Critical Thinking Skills –
Through in-class exercises and homework assignments, students will practice using Excel to analyze data and practice interpreting the meaning of their statistical analysis.
Course Objectives:
I aim to provide you an excellent place to develop your ability to use statistics and probability theory to solve problems. To solve problems well you will need to:
1)understand a range of statistical tools, so that you are able to select the right tool to solve a particular problem.
2)understand statistical principles, so that you can laterlearn to use statistical tools outside the range of the course.
3)have a practical knowledge of how to put particular tools to use, for example with Excel.
4)have practice applying tools to the analysis of real data, to develop the intuition and creativity that are crucial to making your understanding of statistics useful.
Text:
David M. Levine, David Stephan, Timothy Krehbiel, and Mark L. Berenson, Statistics for Managers Using Microsoft Excel, Fifth Edition
The Study Guide and Student Solutions Manual, by Pin Ng, provides some “work” and answers for even-numbered problems in the text [The text also provides answers to even problems, but more sketchy ones.]. The Manual’s purchase is optional; it is also available on reserve in the library.
Most reading will be from Levine, Stephan, Krehbiel, and Berenson (LSK&B henceforth). For any reading assigned, I will expect you to work through included Excel exercises or simulations. Tentative reading assignments for each class meeting are listed below. Readingslisted to the right of a particular date should be completed before coming to class on that date.
Course Requirements:
Your course grade will be an average of your grades for class participation, for homework, for a paper reporting on a use of statistics to test a hypothesis, and for exams. The weights to compute that average follow:
10% / class participation15% / homework
25% / inferential statistics paper
20% / midterm exam
30% / final exam
100%
I expect and encourage all students to participate during class. Your participation will contribute both to your understanding of statistics and to other students' enjoyment and understanding of the course. It will also help me be cheerful, fully engaged, and enthusiastic. Fabulous participation would involve doing the reading with care, attending class regularly, coming to class on time, completing in-class work in a conscientious manner, asking questions to clarify points I make or points made in class reading, and fulfilling well your duties when you are a member of the Early Crew. If you speak up very seldom and have a careless attitude toward in-class work, you will get a failing grade in participation.
Class preparation will be judged according to the homework you turn in. Homework assignments will normally be emailed to students one week before they are due. Some of the problems assigned as homework will require that you use concepts in the reading for the day the homework is due. When homework has been assigned for a particular day, that day’s class will begin with me collecting homework. To get full credit on the homework, you will need to
a) hand it in at the beginning of class, or send it to me in advance of class,
b) get correct, or nearly correct, answers,
c) show your work and give all explanations in your own words (For example, do not directly quote our textbook, even if you use quotation marks.), and
d) organize your work well and present it neatly.
Full credit on a piece of homework will be 12 points. If you cannot make any progress toward solving a problem assigned, you will get half credit if you provide a careful explanation of your confusion. At the end of the semester, I will drop your lowest homework score. Your homework grade will be the sum of the points you earn on the remaining 9 homework assignments (90 points will be an A, 80 points a B, and so on).
Once during the semester, you will be a member of the Early Crew (see the schedule below). On that occasion, your homework will be due early, by Saturday at 1 pm (rather than at the beginning of class). When you are on the Early Crew, your job is to tell me about anything unclear, or particularly difficult, with the homework. You should do that when you turn the homework in. Better yet, tell me earlier. This feedback will allow me to send an email to your classmates praising you as a hero and providing clarifications or hints to them. If you cannot fulfill your responsibilities as a member of the Early Crew, it is your responsibility to recruit a classmate to switch with you. After you find someone who is willing, both of you should send me an email notifying me of the switch.
Early Crew Schedule
Homework tobe turned in early / #1 / #2 / #3 / #4 / #5 / #6 / #7 / #8 / #9 / #10
Last names beginning with / A to An / Ao to Br / Bs to Ek / El to Kn / Ko to Li / Lj to Mc / Md to Ph / Pi to Si / Sj to Ta / Tb to Z
For most students, good performance in the class requires doing problems in addition to those included in homework. In particular, some students need ongoing practice with each new statistical technique introduced. Make ongoing review part of your study strategy. If you find your command of a technique is slipping, go back and do some problems from the relevant section of the textbook.
The inferential statistics paper is an opportunity to practice a) creative, careful use of statistical tools and b) clear communication about statistical results. Due dates for the paper are on the class schedule below. An assignment sheet for the paper will be provided later; it will give detailed information on the requirements for the assignment. For the paper, students should take care to use footnotes to cite every source drawn upon—including the source of the data analyzed. If they borrow a phrase or sentence from a source, students should also use quotation marks to indicate this. Failure to do these things is considered cheating. Our policy on cheating is presented below.
Exams will consist mostly of problems to be worked and/or explained. Some problems will need to be worked with Excel. The final exam will be comprehensive. Past exams can be reviewed on the class web site.
University and School of Business Policy on Cheating:
“Because of the University’s commitment to academic integrity, cheating on course work or on examinations will result in penalties that may include a grade of “F” for the specific exam or course work or a grade of “F” for the course. Any incident of cheating will be reported to the dean of the college in which the course is offered and to the dean of the college or school in which the student is currently enrolled” (University of PortlandBulletin). Students in the School of Business Administration who are turned in for an initial case of cheating will be put on probation. A second cheating incident will lead to dismissal from the School of Business. Note: Plagiarism is considered to be a form of cheating. It consists of taking the ideas, writings, etc. from another and passing them off as one’s own (Webster’s New World Dictionary).
Honors Pledge:
All academic work done at the University of Portland must be in full compliance with the University’s Code of Academic Integrity as described in the Student Handbook. The Pamplin School requires all students to include the following pledge and student signature(s) on all work (papers, exams, etc.) submitted to the professor during the course of the semester (please place it on the front page of all submissions). If you are turning in a group paper, it is incumbent upon all members of the group to sign the pledge. This means every individual in the group is responsible for the integrity of the group’s assignment. Any paper turned in without the pledge and the appropriate signature(s) will be returned immediately to the student(s). It will be left to the professor’s discretion on whether or not to assess a penalty for late submission.
Honors Pledge:
As a student of the Dr. Robert B. Pamplin Jr. School of Business Administration I have read, and strive to uphold, the University’s Code of Academic Integrity. I pledge on my honor that I have not given, received, or used any unauthorized materials or assistance on this examination or assignment. I further pledge that I have not engaged in cheating, forgery, or plagiarism and I have cited all appropriate sources.
Student Signature: ______
Students with Disabilities:
If you have a disability and require an accommodation to fully participate in this class, contact the Office for Students with Disabilities (OSWD), located in the University Health Center (503-943-7134) as soon as possible.
If you have an OSWD Accommodation Plan, you should make an appointment to meet with me to discuss your accommodations. Also, you should meet with me if you wish to discuss emergency medical information or special arrangements in case the building must be evacuated.
Course Calendar
Class, Day, Date / Topics, Reading, Paper Deadlines, Exams, Homework1, Wed.
Sept. 2 / Introducing Statistics & Variable Types
Reading:LSK&B Chapter 1, pp. 1-13
Tables and Charts—frequency distributions & histograms, contingency tables using the Pivot Table command, scatter plots,
Reading:LSK&B Chapter 2, pp. 31-32, 44-50, 54-60;EC 2.1, 2.7, 2.8, 2.10 (EC stands for Excel Companion; it’s located at the end of each chapter.)
Descriptive Statistics—an introduction to excellence in graphs and tables
Reading:LSK&B Chapter 2, pp. 62-66
2, Wed.
Sept. 9 / Descriptive Statistics—measuring central tendency & dispersion (for samples and populations), measuring locations away from the center (e.g. quartiles), identifying outliers (in statistical process control & elsewhere), measuring shape
Reading:LSK&B Chapter 3, pp. 95-125, 133-134; EC 3.1, 3.3
Probability & Probability Distributions—the probability of simple events, intersections (joint events), & unions; conditional probabilities
Reading:LSK&B Chapter 4, pp. 147-171
Homework #1
3, Wed.
Sept. 16 / Probability & Probability Distributions—random variables, the binomial probability distribution, the Poisson probability distribution
Reading:LSK&B Chapter 5, pp. 179-184, 189-201; EH.4.3
The Normal Probability Distribution—the normal probability distribution,
the standardized normal distribution, assessing normality, finding probabilities for normal random variables,
Reading:LSK&B, Chapter 6, pp. 217-240
Homework #2
4, Wed.
Sept. 23 / Sampling methods & Survey error
Reading: LSK&B, Chapter7, pp. 251-260
The Sampling Distribution of the Mean—the standard error of the mean, the shape of sampling distributions for normal & not-normal populations
Reading: LSK&B, Chapter 7, pp. 261-271; EC 7.1 (Using ToolPak Random Number Generation)
The Sampling Distribution of the Proportion—the standard error of the proportion, what it takes to get a near-normal sampling distribution for the proportion
Reading:LSK&B, Chapter 7, pp. 272-273;
Homework #3
5, Wed.
Sept. 30 / Inference About the Population Mean—establishing a confidence interval for the population mean when the population standard deviation is known & not known
Reading:LSK&B, Chapter 8, pp. 283-294
Inference About the Population Proportion—establishing a confidence interval for the population proportion, sampling error
Reading:LSK&B Chapter 8, pp. 296-298
Homework #4
6, Wed.
Oct. 7 / Testing Hypotheses About the Population Mean when we know —the null & alternative hypotheses, testing a null hypothesis about the population mean (using two-tailed and
one-tailedtests)
Reading:LSK&B Chapter 9, pp. 327-345
Testing Hypotheses About the Population Mean when we do not know
Reading:LSK&B Chapter 9, pp. 346-350
Testing Hypotheses About the Population Proportion
Reading:LSK&B Chapter 9, pp. 353-359
Homework #5
7, Wed.
Oct. 14 / Chi-Square Distribution—testing for a difference between two, or among more than two, population proportions; dealing with small cell sizes
Reading: LSK&B Chapter 12, pp. 461-474; EC 12.1
Midterm Exam, last 1:20 of class
Oct. 19-23 / Fall Vacation
8, Wed.
Oct. 28 / Chi-Square Distribution—the Marascuilo procedure, testing for independence between two categorical variables
Reading: LSK&B Chapter 12, pp. 474-481; EC 12.2-12.3
Simple Regression as Description—the least-squares method (get a feel for it with the Visual Explorations in Statistics Simple Linear Regression procedure noted on p. 517.), interpreting the estimated intercept and slope, the coefficient of determination (R2)
Reading:LSK&B Chapter 13, pp. 511-527
Proposal for inferential statistics project due (Send me an email by today at the latest. It should tell me your research question, the data set you will use to answer it, and the statistical technique you will use to analyze the data. If your data are on the WWW, it should also send include a link to them.)
Homework #6
9, Wed.
Nov. 4 / Simple Regression as Inference—the standard error of the estimate, regression assumptions and residual analysis, Durbin-Watson statistic, testing inferences about the population slope, pitfalls of regression
Reading:LSK&B Chapter 13, pp. 528-543, 550-555
Multiple Regression—interpreting coefficients, understanding adjusted R2
Reading:LSK&B Chapter 14, pp. 571-578
Homework #7
10, Wed.
Nov. 11 / Multiple Regression— Ftest, residual analysis, statistical significance for slope coefficients (t tests), coefficient of partial determination
Reading:LSK&B Chapter 14, pp. 579-591, EC 13.1-13.4
Homework #8
11, Wed.
Nov. 18 / Multiple Regression—dummy variables, interaction terms
Reading:LSK&B Chapter 14, pp. 592-603
Multiple Regression—quadratic regression models
Reading:LSK&B Chapter 15, pp. 613-620
Homework #9
Nov. 25
(4 pm)
to Nov. 27 / Thanksgiving Vacation
Between 11/2912/2 / Extra Office Hours
Make an appointment; then, bring a rough draft of your paper and get feedback.
12, Wed.
Dec. 2 / Multiple Regression—transformed exponential model (natural logarithm transformation), collinearity, model building
Reading:LSK&B Chapter 15, pp. 622-636
“Spreadsheet on regression using a natural log transformation,”at <http://lewis.up.edu/bus/easton/Bus550.htm>
Homework #10
13, Wed.
Dec. 9 / Final draft of inferential statistics paper due
Oral presentations of some fine inferential statistics papers
Wrapping up: What is fundamental?
14, Wed.
Dec. 16 /