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An edition of Business Analytics, Global Edition (1920)

Business Analytics, Global Edition

by Evans, James R.

Cover of: Business Analytics, Global Edition by Evans, James R., Evans, James R.

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June 18, 2025 | History

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An edition of Business Analytics, Global Edition (1920)

Business Analytics, Global Edition

by Evans, James R.

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Publish Date

1920

Publisher

Pearson Education, Limited

Language

English

Pages

704

Check nearby libraries

WorldCat

Buy this book

Previews available in: English

Edition	Availability
1 Business Analytics, Global Edition 2016, Pearson Higher Education & Professional Group in English 129209544X 9781292095448	zzzz Locate
2 Business Analytics, Global Edition 1920, Pearson Education, Limited in English 1292339063 9781292339061	aaaa Read Listen Locate

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Book Details

Table of Contents

About the Author

Part 1. Foundations of Business Analytics

Chapter 1. Introduction to Business Analytics

Learning Objectives

What Is Business Analytics?

Using Business Analytics

Impacts and Challenges

Evolution of Business Analytics

Analytic Foundations

Modern Business Analytics

Software Support and Spreadsheet Technology

Analytics in Practice: Social Media Analytics

Descriptive, Predictive, and Prescriptive Analytics

Analytics in Practice: Analytics in the Home Lending and Mortgage Industry

Data for Business Analytics

Data Reliability and Validity

Models in Business Analytics

Descriptive Models

Predictive Models

Prescriptive Models

Model Assumptions

Uncertainty and Risk

Problem Solving with Analytics

Recognizing a Problem

Defining the Problem

Structuring the Problem

Analyzing the Problem

Interpreting Results and Making a Decision

Implementing the Solution

Analytics in Practice: Developing Effective Analytical Tools at Hewlett-Packard

Chapter 1 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Appendix A1. Basic Excel Skills

Excel Formulas and Addressing

Copying Formulas

Useful Excel Tips

Excel Functions

Basic Excel Functions

Functions for Specific Applications

Insert Function

Date and Time Functions

Miscellaneous Excel Functions and Tools

Problems and Exercises

Chapter 2. Database Analytics

Learning Objectives

Data Sets and Databases

Using Range Names in Databases

Analytics in Practice: Using Big Data to Monitor Water Usage in Cary, North Carolina

Data Queries: Tables, Sorting, and Filtering

Sorting Data in Excel

Pareto Analysis

Database Functions

Analytics in Practice: Discovering the Value of Database Analytics at Allders International

Logical Functions

Lookup Functions for Database Queries

Excel Template Design

Data Validation Tools

PivotTable Customization

Chapter 2 Technology Help

Problems and Exercises

Case: People's Choice Bank

Case: Drout Advertising Research Project

Part 2. Descriptive Analytics

Chapter 3. Data Visualization

Learning Objectives

The Value of Data Visualization

Tools and Software for Data Visualization

Analytics in Practice: Data Visualization for the New York City Police Department's Domain Awareness System

Creating Charts in Microsoft Excel

Column and Bar Charts

Data Label and Data Table Chart Options

Scatter Charts and Orbit Charts

Combination Charts

Charts from PivotTables

Geographic Data

Other Excel Data Visualization Tools

Analytics in Practice: Driving Business Transformation with IBM Business Analytics

Chapter 3 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Appendix A3. Additional Tools for Data Visualization

Hierarchy Charts

Waterfall Charts

Problems and Exercises

Chapter 4. Descriptive Statistics

Learning Objectives

Analytics in Practice: Applications of Statistics in Health Care

Metrics and Data Classification

Frequency Distributions and Histograms

Frequency Distributions for Categorical Data

Relative Frequency Distributions

Frequency Distributions for Numerical Data

Grouped Frequency Distributions

Cumulative Relative Frequency Distributions

Constructing Frequency Distributions Using PivotTables

Percentiles and Quartiles

Cross-Tabulations

Descriptive Statistical Measures

Populations and Samples

Statistical Notation

Measures of Location: Mean, Median, Mode, and Midrange

Using Measures of Location in Business Decisions

Measures of Dispersion: Range, Interquartile Range, Variance, and Standard Deviation

Chebyshev's Theorem and the Empirical Rules

Standardized Values (Z-Scores)

Coefficient of Variation

Measures of Shape

Excel Descriptive Statistics Tool

Computing Descriptive Statistics for Frequency Distributions

Descriptive Statistics for Categorical Data: The Proportion

Statistics in PivotTables

Measures of Association

Excel Correlation Tool

Using Descriptive Statistics to Analyze Survey Data

Statistical Thinking in Business Decisions

Variability in Samples

Analytics in Practice: Applying Statistical Thinking to Detecting Financial Problems

Chapter 4 Technology Help

Problems and Exercises

Case: Drout Advertising Research Project

Case: Performance Lawn Equipment

Appendix A4. Additional Charts for Descriptive Statistics in Excel for Windows

Problems and Exercises

Chapter 5. Probability Distributions and Data Modeling

Learning Objectives

Basic Concepts of Probability

Experiments and Sample Spaces

Combinations and Permutations

Probability Definitions

Probability Rules and Formulas

Joint and Marginal Probability

Conditional Probability

Random Variables and Probability Distributions

Discrete Probability Distributions

Expected Value of a Discrete Random Variable

Using Expected Value in Making Decisions

Variance of a Discrete Random Variable

Bernoulli Distribution

Binomial Distribution

Poisson Distribution

Analytics in Practice: Using the Poisson Distribution for Modeling Bids on Priceline

Continuous Probability Distributions

Properties of Probability Density Functions

Uniform Distribution

Normal Distribution

The NORM.INV Function

Standard Normal Distribution

Using Standard Normal Distribution Tables

Exponential Distribution

Triangular Distribution

Data Modeling and Distribution Fitting

Goodness of Fit: Testing for Normality of an Empirical Distribution

Analytics in Practice: The Value of Good Data Modeling in Advertising

Chapter 5 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 6. Sampling and Estimation

Learning Objectives

Statistical Sampling

Sampling Methods

Analytics in Practice: Using Sampling Techniques to Improve Distribution

Estimating Population Parameters

Unbiased Estimators

Errors in Point Estimation

Understanding Sampling Error

Sampling Distributions

Sampling Distribution of the Mean

Applying the Sampling Distribution of the Mean

Interval Estimates

Confidence Intervals

Confidence Interval for the Mean with Known Population Standard Deviation

The t-Distribution

Confidence Interval for the Mean with Unknown Population Standard Deviation

Confidence Interval for a Proportion

Additional Types of Confidence Intervals

Using Confidence Intervals for Decision Making

Data Visualization for Confidence Interval Comparison

Prediction Intervals

Confidence Intervals and Sample Size

Chapter 6 Technology Help

Problems and Exercises

Case: Drout Advertising Research Project

Case: Performance Lawn Equipment

Chapter 7. Statistical Inference

Learning Objectives

Hypothesis Testing

Hypothesis-Testing Procedure

One-Sample Hypothesis Tests

Understanding Potential Errors in Hypothesis Testing

Selecting the Test Statistic

Finding Critical Values and Drawing a Conclusion

Two-Tailed Test of Hypothesis for the Mean

Summary of One-Sample Hypothesis Tests for the Mean

One-Sample Tests for Proportions

Confidence Intervals and Hypothesis Tests

An Excel Template for One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Two-Sample Tests for Differences in Means

Two-Sample Test for Means with Paired Samples

Two-Sample Test for Equality of Variances

Analysis of Variance (ANOVA)

Assumptions of ANOVA

Chi-Square Test for Independence

Cautions in Using the Chi-Square Test

Chi-Square Goodness of Fit Test

Analytics in Practice: Using Hypothesis Tests and Business Analytics in a Help Desk Service Improvement Project

Chapter 7 Technology Help

Problems and Exercises

Case: Drout Advertising Research Project

Case: Performance Lawn Equipment

Part 3. Predictive Analytics

Chapter 8. Trendlines and Regression Analysis

Learning Objectives

Modeling Relationships and Trends in Data

Analytics in Practice: Using Predictive Trendline Models at Procter & Gamble

Simple Linear Regression

Finding the Best-Fitting Regression Line

Using Regression Models for Prediction

Least-Squares Regression

Simple Linear Regression with Excel

Regression as Analysis of Variance

Testing Hypotheses for Regression Coefficients

Confidence Intervals for Regression Coefficients

Residual Analysis and Regression Assumptions

Checking Assumptions

Multiple Linear Regression

Analytics in Practice: Using Linear Regression and Interactive Risk Simulators to Predict Performance at Aramark

Building Good Regression Models

Correlation and Multicollinearity

Practical Issues in Trendline and Regression Modeling

Regression with Categorical Independent Variables

Categorical Variables with More Than Two Levels

Regression Models with Nonlinear Terms

Chapter 8 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 9. Forecasting Techniques

Learning Objectives

Analytics in Practice: Forecasting Call-Center Demand at L.L.Bean

Qualitative and Judgmental Forecasting

Historical Analogy

The Delphi Method

Indicators and Indexes

Statistical Forecasting Models

Forecasting Models for Stationary Time Series

Moving Average Models

Error Metrics and Forecast Accuracy

Exponential Smoothing Models

Forecasting Models for Time Series with a Linear Trend

Double Exponential Smoothing

Regression-Based Forecasting for Time Series with a Linear Trend

Forecasting Time Series with Seasonality

Regression-Based Seasonal Forecasting Models

Holt-Winters Models for Forecasting Time Series with Seasonality and No Trend

Holt-Winters Models for Forecasting Time Series with Seasonality and Trend

Selecting Appropriate Time-Series-Based Forecasting Models

Regression Forecasting with Causal Variables

The Practice of Forecasting

Analytics in Practice: Forecasting at NBCUniversal

Chapter 9 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 10. Introduction to Data Mining

Learning Objectives

The Scope of Data Mining

Cluster Analysis

Measuring Distance Between Objects

Normalizing Distance Measures

Clustering Methods

An Intuitive Explanation of Classification

Measuring Classification Performance

Classification Techniques

Cause-and-Effect Modeling

Analytics in Practice: Successful Business Applications of Data Mining

Chapter 10 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 11. Spreadsheet Modeling and Analysis

Learning Objectives

Analytics in Practice: Using Spreadsheet Modeling and Analysis at Nestlé

Model-Building Strategies

Building Models Using Logic and Business Principles

Building Models Using Influence Diagrams

Building Models Using Historical Data

Model Assumptions, Complexity, and Realism

Implementing Models on Spreadsheets

Spreadsheet Design

Spreadsheet Quality

Data Validation

Analytics in Practice: Spreadsheet Engineering at Procter & Gamble

Descriptive Spreadsheet Models

Staffing Decisions

Single-Period Purchase Decisions

Overbooking Decisions

Analytics in Practice: Using an Overbooking Model at a Student Health Clinic

Retail Markdown Decisions

Predictive Spreadsheet Models

New Product Development Model

Retirement Planning

Project Management

Prescriptive Spreadsheet Models

Portfolio Allocation

Locating Central Facilities

Analyzing Uncertainty and Model Assumptions

What-If Analysis

Scenario Manager

Chapter 11 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 12. Simulation and Risk Analysis

Learning Objectives

Monte Carlo Simulation

Random Sampling from Probability Distributions

Generating Random Variates using Excel Functions

Discrete Probability Distributions

Uniform Distributions

Exponential Distributions

Normal Distributions

Binomial Distributions

Triangular Distributions

Monte Carlo Simulation in Excel

Profit Model Simulation

New Product Development

Retirement Planning

Single-Period Purchase Decisions

Overbooking Decisions

Project Management

Analytics in Practice: Implementing Large-Scale Monte Carlo Spreadsheet Models

Dynamic Systems Simulation

Simulating Waiting Lines

Analytics in Practice: Using Systems Simulation for Agricultural Product Development

Chapter 12 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Part 4. Prescriptive Analytics

Chapter 13. Linear Optimization

Learning Objectives

Optimization Models

Analytics in Practice: Using Optimization Models for Sales Planning at NBC

Developing Linear Optimization Models

Identifying Decision Variables, the Objective, and Constraints

Developing a Mathematical Model

More About Constraints

Implementing Linear Optimization Models on Spreadsheets

Excel Functions to Avoid in Linear Optimization

Solving Linear Optimization Models

Solver Answer Report

Graphical Interpretation of Linear Optimization with Two Variables

How Solver Works

How Solver Creates Names in Reports

Solver Outcomes and Solution Messages

Unique Optimal Solution

Alternative (Multiple) Optimal Solutions

Unbounded Solution

Applications of Linear Optimization

Blending Models

Dealing with Infeasibility

Portfolio Investment Models

Scaling Issues in Using Solver

Transportation Models

Multiperiod Production Planning Models

Multiperiod Financial Planning Models

Analytics in Practice: Linear Optimization in Bank Financial Planning

Chapter 13 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 14. Integer and Nonlinear Optimization

Learning Objectives

Integer Linear Optimization Models

Models with General Integer Variables

Workforce-Scheduling Models

Alternative Optimal Solutions

Models with Binary Variables

Using Binary Variables to Model Logical Constraints

Applications in Supply Chain Optimization

Analytics in Practice: Supply Chain Optimization at Procter & Gamble

Nonlinear Optimization Models

A Nonlinear Pricing Decision Model

Quadratic Optimization

Practical Issues Using Solver for Nonlinear Optimization

Analytics in Practice: Applying Nonlinear Optimization at Prudential Securities

Non-Smooth Optimization

Evolutionary Solver

Evolutionary Solver for Sequencing and Scheduling Models

The Traveling Salesperson Problem

Chapter 14 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Chapter 15. Optimization Analytics

Learning Objectives

What-If Analysis for Optimization Models

Solver Sensitivity Report

Using the Sensitivity Report

Interpreting Solver Reports for Nonlinear Optimization Models

Models with Bounded Variables

Auxiliary Variables for Bound Constraints

What-If Analysis for Integer Optimization Models

Visualization of Solver Reports

Using Sensitivity Information Correctly

Chapter 15 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Part 5. Making Decisions

Chapter 16. Decision Analysis

Learning Objectives

Formulating Decision Problems

Decision Strategies Without Outcome Probabilities

Decision Strategies for a Minimize Objective

Decision Strategies for a Maximize Objective

Decisions with Conflicting Objectives

Decision Strategies with Outcome Probabilities

Average Payoff Strategy

Expected Value Strategy

Evaluating Risk

Decision Trees and Risk

Sensitivity Analysis in Decision Trees

The Value of Information

Decisions with Sample Information

Utility and Decision Making

Constructing a Utility Function

Exponential Utility Functions

Analytics in Practice: Using Decision Analysis in Drug Development

Chapter 16 Technology Help

Problems and Exercises

Case: Performance Lawn Equipment

Classifications

Library of Congress: , HD38.7

Edition Identifiers

Open Library: OL29501395M
Internet Archive: businessanalytic0000evan_k8x7
ISBN 13: 9781292339061

Work Identifiers

Work ID: OL21104365W

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