Edition: 2

Pages: 208

## \$40.00

ISBN 9798765754320

Details Electronic Delivery EBOOK 180 days

’Excel’ in Business Analytics has been designed for aspiring business professionals who have an interest in learning the foundation of business analytics within Microsoft Excel. This book assumes no prior working knowledge of descriptive, predictive, or prescriptive analytics or even Microsoft Excel. From a learning perspective, ‘Excel’ In Business Analytics has been developed to support active learning. Thus, the text within the book has been intentionally kept to a minimum and was written in a lean manner with the purpose of quickly acclimating readers to the importance of each topic being presented. There are easy-to-understand definitions and easy-to-find information about equations and formulas that are featured within the examples and highlighted throughout each chapter.

This edition is supported with 72 learning activity videos, which will provide more in-depth discussions about the topics featured within the manuscript. In addition, starting files are provided with the data necessary for you to work along with the videos to complete the activities presented. This hands-on approach will allow you to actively practice and further develop skills related to business analytics as you watch the videos at your own pace.

Instructors adopting this book will have access to multiple choice questions, homework assignments, and exam problems. Those interested in adopting this book should contact Sean Skinner (sskinner@kendallhunt.com) at Kendall Hunt Publishing Company for more information.

PREFACE
THE AUTHORS

CHAPTER 1 DESCRIPTIVE STATISTICS—Building Excel Skills
Professional Formatting Guidance
Paste Special
Shortcut Keys
Order of Operations
Working With Formulas
Cell Referencing
Date and Text Functions
SUMPRODUCT Function
TRANSPOSE Function
IF Statements
Conditional Formatting
INDEX and MATCH Functions
VLOOKUP Function
Working With Macros

CHAPTER 2 DESCRIPTIVE STATISTICS—Summarizing Data
Types of Data and Measurement Scales
Building Summary Tables
Counting the Number of Occurrences
Counting With Continuous Criteria
Summing the Values in a Range
Extreme Values
Averages
Population and Sample Means
Population and Sample Medians
Population and Sample Modes
Weighted Mean
Geometric Mean
The Emperical Rule

CHAPTER 3 DESCRIPTIVE STATISTICS—Data Visualization
Pivot Tables and Pivot Charts
Qualitative Data
Quantitative Data
Bivariate Data
Box and Whisker Plots
Pareto Charts
Reference

CHAPTER 4 INFERENTIAL STATISTICS—Probability
Basic Definitions
Assigning Probability for One Event Experiments
Assigning Probability for Two or More Event Experiments
Principles of Counting
Probability Distributions: Definitions
The Binomial Probability Distribution
Binomial Probability Distribution Example Problem
Hypergeometric Probability Distribution
Hypergeometric Probability Distribution Example Problem
Poisson Probability Distribution
Poisson Probability Distribution Example Problem
Uniform Probability Distribution
Uniform Probability Distribution Example Problem
Exponential Probability Distribution
Exponential Probability Distribution Example Problem
Observational vs. Experimental Studies
Sampling Methods
Central Limit Theorem (CLT)
Normal Probability Distribution
Sample Size Determination for the Mean
Sample Size Determination for the Mean Example Problem
Normal Probability Distribution Example Problem
Control Charts
Sampling Distribution of the Proportion
Sampling Distribution of the Proportion Example Problem
Confidence Interval Estimate for the Proportion
Confidence Interval Estimate for the Proportion Example Problem
Sample Size Determination for the Proportion
Sample Size Determination for the Proportion Example Problem
Reference

CHAPTER 5 INFERENTIAL STATISTICS—Hypothesis Testing
One-Sample Hypothesis Tests for Means
Single-Sample Problem and Solution Example
One Sample Hypothesis Tests for Proportions
One Sample Hypothesis Tests for Proportions Example Problem
Two-Sample, Hypothesis Tests for Means
Two-Sample Problem and Solution Example
Two Sample Hypothesis Tests for Proportions
Multi-Sample or Multi-Factor Hypothesis Tests—for Means
Multi-Sample Problem and Solution Example
Two-Sample Hypothesis Tests for Equal Variances
P-Values in Hypothesis Testing
ANOVA Post Hoc Analysis

CHAPTER 6 PREDICTIVE ANALYTICS—Regression
Contingency Table
Correlation Analysis
Correlation Analysis Problem and Solution Example
Dealing With Missing Data and Outliers
Dealing With Text Data
Simple Regression
Simple Regression Problem and Solution Example
Multiple Regression
Multiple Regression Stepwise Procedure
Multiple Regression Problem and Solution Example

CHAPTER 7 PREDICTIVE ANALYTICS—Time Series
Secular Trend
Cyclical Variations
Seasonal Variations
Irregular Variations
Simple Moving Averages
Weighted Moving Averages
Forecasting Using Exponential Smoothing
Exponential Smoothing Problem and Solution Example
Autoregression
Measures of Error
Reference

CHAPTER 8 PRESCRIPTIVE ANALYTICS—Decision Theory
Payoff Matrix
The Maximum Expected Monetary Value (EMV) Strategy
The MAXIMIN Decision Strategy
The MAXIMAX Decision Strategy
The Regret Matrix and Expected Opportunity Loss (EOL)
The MINIMAX REGRET Decision Strategy
Expected Value of Perfect Information (EVPI)
Decision Trees

CHAPTER 9 PRESCRIPTIVE ANALYTICS—Optimization
Steps to Build an Optimization Model in Excel
Example Production—Transportation Type Optimization Problem
Example Production—Inventory Type Optimization Problem

INDEX

Andrew Goodnite
WILLIAM A. YOUNG

’Excel’ in Business Analytics has been designed for aspiring business professionals who have an interest in learning the foundation of business analytics within Microsoft Excel. This book assumes no prior working knowledge of descriptive, predictive, or prescriptive analytics or even Microsoft Excel. From a learning perspective, ‘Excel’ In Business Analytics has been developed to support active learning. Thus, the text within the book has been intentionally kept to a minimum and was written in a lean manner with the purpose of quickly acclimating readers to the importance of each topic being presented. There are easy-to-understand definitions and easy-to-find information about equations and formulas that are featured within the examples and highlighted throughout each chapter.

This edition is supported with 72 learning activity videos, which will provide more in-depth discussions about the topics featured within the manuscript. In addition, starting files are provided with the data necessary for you to work along with the videos to complete the activities presented. This hands-on approach will allow you to actively practice and further develop skills related to business analytics as you watch the videos at your own pace.

Instructors adopting this book will have access to multiple choice questions, homework assignments, and exam problems. Those interested in adopting this book should contact Sean Skinner (sskinner@kendallhunt.com) at Kendall Hunt Publishing Company for more information.

PREFACE
THE AUTHORS

CHAPTER 1 DESCRIPTIVE STATISTICS—Building Excel Skills
Professional Formatting Guidance
Paste Special
Shortcut Keys
Order of Operations
Working With Formulas
Cell Referencing
Date and Text Functions
SUMPRODUCT Function
TRANSPOSE Function
IF Statements
Conditional Formatting
INDEX and MATCH Functions
VLOOKUP Function
Working With Macros

CHAPTER 2 DESCRIPTIVE STATISTICS—Summarizing Data
Types of Data and Measurement Scales
Building Summary Tables
Counting the Number of Occurrences
Counting With Continuous Criteria
Summing the Values in a Range
Extreme Values
Averages
Population and Sample Means
Population and Sample Medians
Population and Sample Modes
Weighted Mean
Geometric Mean
The Emperical Rule

CHAPTER 3 DESCRIPTIVE STATISTICS—Data Visualization
Pivot Tables and Pivot Charts
Qualitative Data
Quantitative Data
Bivariate Data
Box and Whisker Plots
Pareto Charts
Reference

CHAPTER 4 INFERENTIAL STATISTICS—Probability
Basic Definitions
Assigning Probability for One Event Experiments
Assigning Probability for Two or More Event Experiments
Principles of Counting
Probability Distributions: Definitions
The Binomial Probability Distribution
Binomial Probability Distribution Example Problem
Hypergeometric Probability Distribution
Hypergeometric Probability Distribution Example Problem
Poisson Probability Distribution
Poisson Probability Distribution Example Problem
Uniform Probability Distribution
Uniform Probability Distribution Example Problem
Exponential Probability Distribution
Exponential Probability Distribution Example Problem
Observational vs. Experimental Studies
Sampling Methods
Central Limit Theorem (CLT)
Normal Probability Distribution
Sample Size Determination for the Mean
Sample Size Determination for the Mean Example Problem
Normal Probability Distribution Example Problem
Control Charts
Sampling Distribution of the Proportion
Sampling Distribution of the Proportion Example Problem
Confidence Interval Estimate for the Proportion
Confidence Interval Estimate for the Proportion Example Problem
Sample Size Determination for the Proportion
Sample Size Determination for the Proportion Example Problem
Reference

CHAPTER 5 INFERENTIAL STATISTICS—Hypothesis Testing
One-Sample Hypothesis Tests for Means
Single-Sample Problem and Solution Example
One Sample Hypothesis Tests for Proportions
One Sample Hypothesis Tests for Proportions Example Problem
Two-Sample, Hypothesis Tests for Means
Two-Sample Problem and Solution Example
Two Sample Hypothesis Tests for Proportions
Multi-Sample or Multi-Factor Hypothesis Tests—for Means
Multi-Sample Problem and Solution Example
Two-Sample Hypothesis Tests for Equal Variances
P-Values in Hypothesis Testing
ANOVA Post Hoc Analysis

CHAPTER 6 PREDICTIVE ANALYTICS—Regression
Contingency Table
Correlation Analysis
Correlation Analysis Problem and Solution Example
Dealing With Missing Data and Outliers
Dealing With Text Data
Simple Regression
Simple Regression Problem and Solution Example
Multiple Regression
Multiple Regression Stepwise Procedure
Multiple Regression Problem and Solution Example

CHAPTER 7 PREDICTIVE ANALYTICS—Time Series
Secular Trend
Cyclical Variations
Seasonal Variations
Irregular Variations
Simple Moving Averages
Weighted Moving Averages
Forecasting Using Exponential Smoothing
Exponential Smoothing Problem and Solution Example
Autoregression
Measures of Error
Reference

CHAPTER 8 PRESCRIPTIVE ANALYTICS—Decision Theory
Payoff Matrix
The Maximum Expected Monetary Value (EMV) Strategy
The MAXIMIN Decision Strategy
The MAXIMAX Decision Strategy
The Regret Matrix and Expected Opportunity Loss (EOL)
The MINIMAX REGRET Decision Strategy
Expected Value of Perfect Information (EVPI)
Decision Trees

CHAPTER 9 PRESCRIPTIVE ANALYTICS—Optimization
Steps to Build an Optimization Model in Excel
Example Production—Transportation Type Optimization Problem
Example Production—Inventory Type Optimization Problem

INDEX

Andrew Goodnite
WILLIAM A. YOUNG