Business Statistics II: Using Excel, MegaStat and Minitab |
This web-based course is the second in a two-course series designed to introduce working professionals and students to the concepts of basic statistics as applied to business and economics. The course is intended as a continuation of Business Statistics I or for those with a background in introductory statistical analysis. (The course begins with a thorough discussion of multiple regression analysis.) Taught from an applied perspective, the course utilizes the data analysis tools available in Microsoft Excel, MegaStat, and Minitab 14 which is a powerful statistical package widely used in both industry and academics. Upon completion of the two-course series, the student will have acquired the skills to conduct statistical analyses valuable not only in business and economics, but necessary for other social and physical sciences as well. Course Specifications: |
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Length: |
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8 weeks |
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Weekly time commitment: |
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Approximately 5 to 10 hours |
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Prerequisites: |
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Basic knowledge of Microsoft Excel is recommended. |
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Tuition: |
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US $1,195 |
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Additional expenses: |
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Textbook including MegaStat software
U.S. Edition is approximately $135 (Foreign Edition prices vary by location)
Student version of Minitab 14 is $55.95
Foreign Edition prices vary by location. |
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Course Materials - Text and Software
The course is based upon the text, Statistical Techniques in Business & Economics, by Douglas A. Lind, William G. Marchal, and Samuel A. Wathen. Published by McGraw-Hill Higher Education, the text is in its 12th Edition. MegaStat for Excel is a sophisticated and user-friendly statistical package which accompanies the text.
The text can be purchased directly from McGraw-Hill Higher Education at http://www.mhhe.com/. A student version of Minitab 14 software is available and can be downloaded from www.academicsuperstore.com
System Requirements
The minimum system requirements necessary to complete the course are as follows:
- A 486X-based computer
- Microsoft Windows 95 or later or Windows NT 4.0 or later.
- A minimum of 16MB of memory with 32MB recommended
- A VGA, super VGA or compatible display
- Access to the Internet and an email account with the ability to send and receive large Microsoft Excel and Word files
Course Format
The course runs 8 weeks. Each week the student will complete the assigned readings in the text and review the associated lecture notes and Powerpoints. End-of-chapter exercises and case studies apply the concepts and computational tools to the material introduced in the text. A weekly Personal Assessment will enable the student to test his or her knowledge of the chapter material. Students will also have the opportunity to apply the statistical analysis techniques they have learned to a real world situation of personal or professional interest.
Course Content
Unit 1: Multiple Regression and Correlation Analysis
Unit 1 introduces multiple regression analysis and the use of inference. Assumptions regarding the multiple regression model and correlation are detailed. The ANOVA table is illustrated. Evaluating the regression equation is discussed including use of the scatter diagram, correlation matrix, and the Global test. Evaluating individual regression coefficients is explained.
Unit 2: Nonparametric Methods: Chi-Square Applications
This unit introduces the chi-square statistic and its use for data measured with a nominal scale. Characteristics of the chi-square distribution are outlined and goodness-of-fit tests are demonstrated for equal expected frequencies and unequal expected frequencies. Limitations of chi-square are outlined.
Unit 3: Nonparametric Methods: Analysis of Ranked Data
Methods for analyzing ranked data are detailed including five distribution-free tests and the Spearman coefficient of rank correlation. The tests examined include the sign test, the median test, the Wilcoxon signed-rank test, the Wilcoxon rank-sum test, and the Kruskal-Wallis test of variance by rank.
Unit 4: Statistical Quality Control
This unit introduces the techniques used in statistical quality control. A brief history of quality control is provided. Causes of variation are outlined and the use of diagnostic charts is illustrated. Acceptance sampling is detailed.
Unit 5: Indexing
The use and construction of indexes is detailed in this unit. The purpose for converting data to indexes is explained. Weighted, un-weighted, value, and special-purpose indexes are illustrated.
Unit 6: Time Series and Forecasting
This unit provides an introduction to time series and forecasting. Components of data are examined including trend, cycle, seasonality, and irregular variations. Basic averaging techniques and dealing with trend and seasonality are discussed.
Unit 7: Introduction to Decision Theory
This unit introduces decision making under conditions of uncertainty. Sensitivity analysis, decision trees and various max-min strategies are outlined.
Unit 8: Putting It All Together
This unit reviews the statistical concepts introduced and provides an additional opportunity to apply the data analysis techniques acquired to a variety of problems encountered in the business world. Students may also use this final week of the course to study a situation of personal or professional interest.
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