Office Hours

Office Hours: 8:15-10:00 a.m. MWF or by appointment

TEXT: Berenson, Levine, & Krehbiel Basic Business Statistics, 10thEdition, Prentice Hall, 2006.

PREREQUISITE: BUSA 231, ACCT 212, ECON 201.

COURSE DESCRIPTION: Development of analytical thinking and data deduction skills. Includes analysis of variance and experimental design, nonparametric inference, advanced modeling and forecasting, statistical process control and decision analysis are used as problem solving tools with managerial and research applications. Credit, three hours.

PREAMBLE: This is the third and last class in a three-part sequence. In Quantitative Methods I you learned calculus, which is the basis for all advanced math. Calculus is an excellent way to learn logic. Next was Quantitative Methods II, which covered basic statistics. The third and final course teaches more advanced statistical concepts which have application to all areas of business. Finance, marketing, and production and operations all make extensive use of the statistical test covered in this course. Because of this course has so many applications to other courses it should be taken the semester after completing prerequisites.

GOALS and PHILOSOPHY: My goals are to present statistics in an interesting way, with using real data is much as possible to help you see some of the many ways that statistics can be applied to the business world. I want the environment to be lively and fun, with students asking questions and making observations. I want you to be able to use statistics for accurate analysis of data, to gain insights that are only truly known from the correct application of appropriate statistical techniques, to be able to use statistics to argue persuasively, and to never be fooled by statistics that have been computed to mislead the naive and uninformed.

TIME: The class meets on Tuesday and Thursday in DBH 204 & 312 from 2:30 – 3:45 pm.

LEARNING OBJECTIVES

General Competency: It is to be assumed throughout that all statistical methods can and will be accomplished using a personal computer (usually accessing Minitab software), with the rare exception of software limitations. However, using the definitional formula and working problem without the aid of a computer is the best method to truly understand how the concept works. The personal computer will also be used as a simulation tool to reinforce theoretical concepts through empirical investigation.

Classical Inference: Estimation

1. Know the difference between point and interval estimation.

2. Estimate a population mean from a sample mean for large sample sizes.

3. Estimate a population mean from a sample mean for small sample sizes.

4. Estimate a population proportion from a sample proportion.

5. Estimate the population variance from a sample variance.

6. Estimate the minimum sample size necessary to achieve given statistical goals.

Classical Inference: Hypothesis Testing for Single Populations

1. Understand the logic of hypothesis testing and know how to establish null and alternative hypotheses.

2. Understand Type I and Type II errors, power, and effect size, and how to solve for Type II errors.

3. Use large samples to test hypotheses about a single population mean and a single population proportion.

4. Test hypotheses about a single population mean using small samples when is unknown and the population is normally distributed.

5. Test hypotheses about a single population variance.

Classical Inference: Hypothesis Testing for two Populations

1. Test hypotheses and construct confidence intervals about the difference in two population means using data from large samples.

2. Test hypotheses and establish confidence intervals about the difference in two population means using data from small samples when the population variances are unknown.

3. Test hypotheses and construct confidence intervals about the difference in two related populations.

4. Test hypotheses and construct confidence intervals about the difference in two population proportions.

5. Test hypotheses and construct confidence intervals about two population variances.

Analysis of Variance/Design of Experiments

1. Understand the differences between various experimental designs and when to use them.

2. Compute and interpret the results of a one-way ANOVA.

3. Compute and interpret the results of a random block design ANOVA.

4. Compute and interpret the results of a two-way (factorial) ANOVA.

5. Understand and interpret interaction.

6. Know when and how to use post-hoc multiple comparison techniques.

Bivariate Correlation and Regression

1. Be able to determine the equation of a simple regression line form a sample of data and interpret the slope and intercept of the equation.

2. Be able to understand the usefulness of residual analysis in testing the assumptions underlying regression analysis and in examining the fit of the regression line to the data.

3. Compute a standard error of the estimate and interpret its meaning.

4. Compute a coefficient of determination and interpret it.

5. Test hypotheses about the slope of the regression model and interpret the results.

6. Estimate values of Y using the regression model.

7. Compute a coefficient of correlation and interpret it.

Multiple Regression/Model Development

1. Be able to develop a multiple regression model.

2. Understand and apply techniques that can be used to determine how well a regression model fits the data.

3. Be able to analyze and interpret nonlinear variables in multiple regression analysis.

4. Understand the role of quantitative variables and how to use them in multiple regression analysis.

5. Learn how to build and evaluate multiple regression models.

Forecasting/Time Series

1. Learn how regression analysis can be used to construct forecasting models.

2. Understand the nature of autocorrelation and how to test for it.

3. Understand autoregression in forecasting.

4. Become aware of several ways to measure forecasting error.

5. Understand the nature of time series data.

6. Learn how to forecast form time series data using smoothing techniques and decomposition.

Chi-Square/ Nonparametric Statistics

1. Recognize the advantages and disadvantages of nonparametric statistical methods.

2. Understand the chi-square goodness-of-fit test and how to use it.

3. Analyze data using the chi-square test of independence.

4. Know when and how to use the Mann-Whitney U Test, the Wilcoxon matched-pairs signed rank test, and the Kruskal-Wallis test.

5. Learn when and how to measure correlation-using Spearman's who rank correlation coefficient.

Statistical Quality Control

1. Understand the concepts of quality, quality control, and continuous quality improvement.

2. Understand the importance of statistical quality control in continuous quality improvement.

3. Learn about various quality diagnostic techniques, including Pareto charts, fishbone diagrams, and control charts.

4. Learn how to construct X-bar/R, P, c, and XmR control charts.

5. Understand the theory and application of acceptance sampling.

Decision Analysis

1. Learn about decision making under certainty under uncertainty, and risk.

2. Learn several strategies for decision-making under uncertainty, including expected payoff, and expected opportunity loss.

3. Learn how to construct and analyzed decision trees.

4. Understand aspects of utility theory.

5. Learn how to revise probabilities with sample information.

ASSESSMENT AND GRADING

The exams will be composed of multiple problems. You will have to do computations and explain and interpret results. Each exam during the semester will focus primarily on the material covered since the last exam, however most of what is covered is builds on concepts covered in Quantitative Methods I & II and you should be able to answer questions using those concepts. For example, hypothesis testing uses the sample mean and standard deviation. Mean and standard deviation were covered in your first statistics course but may have to compute them to work the hypothesis test question. The final exam is comprehensive.

The computer is a valuable tool for the statistician. Several problems will be assigned during the semester. These are take home assignment where the computations will be done on the computer. Specific guidelines will be given for each problem.

Activity	Value of Final Course Grade
1st Exam	20.00%
2nd Exam	20.00%
3rd Exam	20.00%
Problems	15.00%
Final Exam	25.00%
TOTAL	100.00%

Assignment

Date	Subject
Tue Aug 27	Introduction
Th Aug 30	Review Normal Distribution and Z chart
Tue Sep 4	Review of Ch 9
Th Sep 7	Ch 9: Hypothesis Testing
Tue Sep 11	Ch 9 & Ch 10	Problem
Th Sep 13	Ch 10: Two Means and two Proportions
Tue Sep 18	Ch 10: continued
Th Sep 20	Ch 11: ANOVA
Tue Sep 25	1^St Exam
Th Sep 27	Ch 11: ANOVA	Problem
Tue Oct 2	Ch 12: Chi Square
Th Oct 4	Ch 12: Goodness of Fit
Tue Oct 9	Ch 12: Goodness Fit
Th Oct 11	Ch:13 Regression	LAST DAY TO DROP WITH A GRADE OF W
Tue Oct 16	Fall Break
Th Oct 18	No Class – Conference in Colorado
Tue Oct 23	Ch 14: Non Parametric
Th Oct 25	Ch 14: Regression	Problem
Tue Oct 30	Ch 15: Regression
Th Nov 1	2nd Exam
Tue Nov 6	Ch 15: Regression
Th Nov 8	Ch 15: Regression	EASTON Realty
Tue Nov 13	Ch 15: Regression
Th Nov 15	Ch 16: Forecasting
Tue Nov 20	Ch 16: Forecasting
Th Nov 22	Ch 20: TQM
Tue Nov 27	Ch 20: TQM
Th Nov 29	3^rd Exam
Tue Dec 4	Trial – Easton Case
Th Dec 6	Catch-up
Tue Dec 11	FINAL EXAM 1:00 – 3:00 p.m.	COMPREHENSIVE FINAL (This is the time and date for everyone in the class; don't ask to take it early)

Attendance Policy

You are expected to attend all classes, more than three absences are considered excessive unless the absences have been authorized by the Provost’s Office.

Grading Scale

A 93+ D+ 67-69

A- 90-92 D 63-66

B+ 87-89 D- 60-62

B 83-86 F BELOW 60

B- 80-82

C+ 77-79

C 73-76

C- 70-72

There will be NO make-up exams, except when an exam is missed due to an absence that has been authorized by the Provost’s Office. Failure to take an exam means a zero, if an emergency arises and I authorized absences, the final will count for the missed exam. Group Projects are due on or before the due date at the beginning of class. A grade of zero may be assigned if the work is not turned in on time.

Topical Coverage Grid

Topical Coverage	Covered
Ethical Issues	Yes
Global Issues	No
Political	No
Social	No
Legal	No
Regulatory	No
Environmental	No
Technological Issues	Yes
Diversity	No
Written Communication	Yes
Oral Communication	No

Americans with Disabilities Act

"Samford University complies with Section 504 of the Rehabilitation Act and the Americans with Disabilities Act. Students with disabilities who seek accommodations must make their request through Disability Support Services. This office is located in Counseling Services on the lower level of Pittman Hall, or can be reached by calling 726-4078 or 726-2105. A faculty member will grant reasonable accommodations only upon notification from the Disability Support Services."

If You Need Help: If you find that you are having particular difficulty with any of the material in this course:

1. DO NOT let it build up. The material is very cumulative in nature and you are likely to find yourself only falling further behind.

2. DO come and see me, either during the assigned office hours or by making an appointment. Be forewarned: I expect that you have read the appropriate sections of the textbook and reviewed your notes BEFORE you come to my office.

Etiquette:

Success in business requires that you understand and adhere to the corporate culture. This culture can vary from company to company and even within a company. Certain behavior may hinder one’s career or even end it with a company. The culture in this class requires that you remove your hat upon entering class. Cell phones will be turned off upon entering class. Taking a cell phone call during class is a serious violation of the corporate culture of this class and will not be tolerated.

Multitasking reduces your ability to concentrate and learn, it also disengages you from classroom discussions and is rude. In the classroom you should focus your attention on the lecture and or discussion. Surfing the web, reading and or writing emails is not appropriate. I have the ability to monitor your computer and you need to remember that any thing that you have on the screen of your computer is subject to display in the class. If you are typing or receiving emails I have the ability to project them to the big screen in the front of the classroom for all to read.

Academic Integrity

We value a campus community that encourages personal growth and academic development in an atmosphere of positive Christian influence. We affirm the necessity of academic standards of conduct that allow student and faculty to live and study together. We value the fair and efficient administration of these standards of conduct.

- Samford University Code of Values

No form of academic dishonesty will be tolerated.