Statistics for Managers Using Microsoft Excel -- ACCUMULATOR

Table of Contents:
F. First Things First

• FTF.1 Think Differently About Statistics
• FTF.2 Business Analytics: The Changing Face of Statistics
• FTF.3 Starting Point for Learning Statistics
• FTF.4 Starting Point for Using Software
• FTF.5 Starting Point for Using Microsoft Excel

• 1.1 Defining Variables
• 1.2 Collecting Data
• 1.3 Types of Sampling Methods
• 1.4 Data Cleaning
• 1.5 Other Data Preprocessing Tasks
• 1.6 Types of Survey Errors

• 2.1 Organizing Categorical Variables
• 2.2 Organizing Numerical Variables
• 2.3 Visualizing Categorical Variables
• 2.4 Visualizing Numerical Variables
• 2.5 Visualizing Two Numerical Variables
• 2.6 Organizing a Mix of Variables
• 2.7 Visualizing a Mix of Variables
• 2.8 Filtering and Querying Data 
• 2.9 Pitfalls in Organizing and Visualizing Variables

• 3.1 Measures of Central Tendency
• 3.2 Measures of Variation and Shape
• 3.3 Exploring Numerical Variables
• 3.4 Numerical Descriptive Measures for a Population
• 3.5 The Covariance and the Coefficient of Correlation
• 3.6 Descriptive Statistics: Pitfalls and Ethical Issues

• 4.1 Basic Probability Concepts
• 4.2 Conditional Probability
• 4.3 Ethical Issues and Probability
• 4.4 Bayes' Theorem
• 4.5 Counting Rules

• 5.1 The Probability Distribution for a Discrete Variable
• 5.2 Binomial Distribution
• 5.3 Poisson Distribution
• 5.4 Covariance of a Probability Distribution and Its Application in Finance
• 5.5 Hypergeometric Distribution

• 6.1 Continuous Probability Distributions
• 6.2 The Normal Distribution
• 6.3 Evaluating Normality
• 6.4 The Uniform Distribution
• 6.5 The Exponential Distribution
• 6.6 The Normal Approximation to the Binomial Distribution

• 7.1 Sampling Distributions
• 7.2 Sampling Distribution of the Mean
• 7.3 Sampling Distribution of the Proportion
• 7.4 Sampling from Finite Populations

• 8.1 Confidence Interval Estimate for the Mean (σ Known)
• 8.2 Confidence Interval Estimate for the Mean (σ Unknown)
• 8.3 Confidence Interval Estimate for the Proportion
• 8.4 Determining Sample Size
• 8.5 Confidence Interval Estimation and Ethical Issues
• 8.6 Application of Confidence Interval Estimation in Auditing
• 8.7 Estimation and Sample Size Determination for Finite Populations
• 8.8 Bootstrapping

• 9.1 Fundamentals of Hypothesis Testing
• 9.2 t Test of Hypothesis for the Mean (σ Unknown)
• 9.3 One-Tail Tests
• 9.4 Z Test of Hypothesis for the Proportion
• 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
• 9.6 Power of the Test

• 10.1 Comparing the Means of Two Independent Populations
• 10.2 Comparing the Means of Two Related Populations
• 10.3 Comparing the Proportions of Two Independent Populations
• 10.4 F Test for the Ratio of Two Variances
• 10.5 Effect Size

• 11.1 One-Way ANOVA
• 11.2 Two-Way ANOVA
• 11.3 The Randomized Block Design
• 11.4 Fixed Effects, Random Effects, and Mixed Effects Models

• 12.1 Chi-Square Test for the Difference Between Two Proportions
• 12.2 Chi-Square Test for Differences Among More Than Two Proportions
• 12.3 Chi-Square Test of Independence
• 12.4 Wilcoxon Rank Sum Test for Two Independent Populations
• 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA
• 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
• 12.7 Chi-Square Test for the Variance or Standard Deviation
• 12.8 Wilcoxon Signed Ranks Test for Two Related Populations

• 13.1 Simple Linear Regression Models
• 13.2 Determining the Simple Linear Regression Equation
• 13.3 Measures of Variation
• 13.4 Assumptions of Regression
• 13.5 Residual Analysis
• 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic
• 13.7 Inferences About the Slope and Correlation Coefficient
• 13.8 Estimation of Mean Values and Prediction of Individual Values
• 13.9 Potential Pitfalls in Regression

• 14.1 Developing a Multiple Regression Model
• 14.2 Evaluating Multiple Regression Models
• 14.3 Multiple Regression Residual Analysis
• 14.4 Inferences About the Population Regression Coefficients
• 14.5 Testing Portions of the Multiple Regression Model
• 14.6 Using Dummy Variables and Interaction Terms
• 14.7 Logistic Regression
• 14.8 Cross-Validation

• 15.1 The Quadratic Regression Model
• 15.2 Using Transformations in Regression Models
• 15.3 Collinearity
• 15.4 Model Building
• 15.5 Pitfalls in Multiple Regression and Ethical Issues

• 16.1 Time-Series Component Factors
• 16.2 Smoothing an Annual Time Series
• 16.3 Least-Squares Trend Fitting and Forecasting
• 16.4 Autoregressive Modeling for Trend Fitting and Forecasting
• 16.5 Choosing an Appropriate Forecasting Model
• 16.6 Time-Series Forecasting of Seasonal Data
• 16.7 Index Numbers

• 17.1 Business Analytics Overview
• 17.2 Descriptive Analytics
• 17.3 Decision Trees
• 17.4 Clustering
• 17.5 Association Analysis
• 17.6 Text Analytics
• 17.7 Prescriptive Analytics

• 18.1 Analyzing Numerical Variables
• 18.2 Analyzing Categorical Variables

• 19.1 The Theory of Control Charts
• 19.2 Control Chart for the Proportion: The p Chart
• 19.3 The Red Bead Experiment: Understanding Process Variability
• 19.4 Control Chart for an Area of Opportunity: The c Chart
• 19.5 Control Charts for the Range and the Mean
• 19.6 Process Capability
• 19.7 Total Quality Management
• 19.8 Six Sigma

• 20.1 Payoff Tables and Decision Trees
• 20.2 Criteria for Decision Making
• 20.3 Decision Making with Sample Information
• 20.4 Utility

David M. Levine is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York). He received B.B.A. and M.B.A. degrees in statistics from City College of New York and a Ph.D. from New York University in industrial engineering and operations research. He is nationally recognized as a leading innovator in statistics education and is the co-author of 14 books, including such best-selling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab.

He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, currently in its second edition, Six Sigma for Green Belts and Champions and Design for Six Sigma for Green Belts and Champions, and the author of Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and Quality Management, third edition, McGraw-Hill/Irwin. He is also the author of Video Review of Statistics and Video Review of Probability, both published by Video Aided Instruction, and the statistics module of the MBA primer published by Cengage Learning. He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Decision Sciences Journal of Innovative Education, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and he has given numerous talks at the Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences. David Levine has also received several awards for outstanding teaching and curriculum development from Baruch College.

David F. Stephan is an independent instructional technologist. He was an Instructor/Lecturer of Computer Information Systems at Baruch College (City University of New York) for over 20 years and also served as an Assistant to the Provost and to the Dean of the School of Business & Public Administration for computing. He pioneered the use of computer classrooms for business teaching, devised interdisciplinary multimedia tools, and created techniques for teaching computer applications in a business context. He also conducted the first large-scale controlled experiment to show the benefit of teaching Microsoft Excel in a business case context to undergraduate students.

An avid developer, he created multimedia courseware while serving as the Assistant Director of a Fund for the Improvement of Postsecondary Education (FIPSE) project at Baruch College. Stephan is also the originator of PHStat, the Pearson Education statistical add-in for Microsoft Excel and a co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics and Practical Statistics by Example Using Microsoft Excel and Minitab. He is currently developing ways to extend the instructional materials that he and his co-authors develop to mobile and cloud computing platforms as well as develop social-media facilitated means to support learning in introductory business statistics courses.

David Stephan received a B.A. in geology from Franklin and Marshall College and a M.S. in computer methodology from Baruch College (City University of New York).

Kathryn A. Szabat is Associate Professor and Chair of Business Systems and Analytics at LaSalle University. She teaches undergraduate and graduate courses in business statistics and operations management. She also teaches as Visiting Professor at the Ecole Superieure de Commerce et de Management (ESCEM) in France.

Szabat’s research has been published in International Journal of Applied Decision Sciences, Accounting Education, Journal of Applied Business and Economics, Journal of Healthcare Management, and Journal of Management Studies. Scholarly chapters have appeared in Managing Adaptability, Intervention, and People in Enterprise Information Systems; Managing, Trade, Economies and International Business; Encyclopedia of Statistics in Behavioral Science; and Statistical Methods in Longitudinal Research.

She has provided statistical advice to numerous business, non-business, and academic communities. Her more recent involvement has been in the areas of education, medicine, and nonprofit capacity building.

Kathryn Szabat received a B.S. in mathematics from State University of New York at Albany and M.S. and Ph.D. degrees in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.