Business Analytics - GBV

Business Analytics

Methods, Models, and Decisions

James R. Evans : University of Cincinnati

PEARSON

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Contents

Preface xiii About the Author xix

Part 1: Foundations of Business Analytics Chapter 1: introduction to Business Analytics 1 Learning Objectives 1 What Is Business Analytics? 3 Evolution of Business Analytics 5 Scope of Business Analytics 6 Data for Business Analytics 8

Data Sets and Databases 10 ? Metrics and Data Classification 10 ? Data Reliability and Validity 13 Decision Models 13 Descriptive Decision Models 15 ?> Predictive Decision Models 19 ? Prescriptive Decision Models 21 Problem Solving and Decision Making 22 Recognizing a Problem 22 ? Defining the Problem 23 ? Structuring the Problem 23 ? Analyzing the Problem 23 ? Interpreting Results and Making a Decision 23 ? Implementing the Solution 24 Key Terms 25 ? Fun with Analytics 25 ? Problems and Exercises 25 ? Case: Performance Lawn Equipment 28

Chapter 2: Analytics on Spreadsheets 31

Learning Objectives 31 Basic Excel Skills 33

Excel Formulas 34 ? Copying Formulas 35 ? Other Useful Excel Tips 36 Excel Functions 37

Basic Excel Functions 37 ? Functions for Specific Applications 38 ? Insert Function 39 ? Logical Functions 39 ? Lookup Functions 41 Spreadsheet Add-Ins for Business Analytics 43 Spreadsheet Modeling and Spreadsheet Engineering 44 Spreadsheet Quality 46 Key Terms 49 ? Problems and Exercises 49 ? Case: Performance LawJT Equipment 51

Part 2: Descriptive Analytics Chapter 3: Visualizing and Exploring Data 53 Learning Objectives 53 Data Visualization 54

Creating Charts in Microsoft Excel 2010 54 ? Miscellaneous Excel Charts 59 ? Geographic Data 60

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Contents

Data Queries: Using Sorting and Filtering 60 Sorting Data in Excel 61 ? Pareto Analysis 61 ? Filtering Data 62

Statistical Methods for Summarizing Data 65 Frequency Distributions for Categorical Data 66 ? Relative Frequency Distributions 68 ? Frequency Distributions for Numerical Data 68 ? Excel Histogram Tool 69 ? Cumulative Relative Frequency Distributions 72 ? Percentiles and Quartiles 73 ? Cross-Tabulations 75

Exploring Data Using PivotTables 77 PivotCharts 81

Key Terms 81 ? Problems and Exercises 82 ? Case: Performance Lawn Equipment 84

Chapter 4: Descriptive Statistical Measures 85

Learning Objectives 85 Populations and Samples 86

Understanding Statistical Notation 86 Measures of Location 87

Arithmetic Mean 87 ? Median 88 ? Mode 89 ? Midrange 90 ? Using Measures of Location in Business Decisions 90 Measures of Dispersion 91 Range 91 ? Interquartile Range 92 ? Variance 92 ? Standard Deviation 93 ? Chebyshev's Theorem and the Empirical Rules 94 Standardized Values 98 ? Coefficient of Variation 99 Measures of Shape 99 Excel Descriptive Statistics Tool 102 Descriptive Statistics for Grouped Data 103 Descriptive Statistics for Categorical Data: The Proportion 106 Statistics in PivotTables 106 Measures of Association 106 Covariance 108 - Correlation 109 ? Excel Correlation Tool 112 Outliers 113 Statistical Thinking in Business Decisions 115 Variability in Samples 116

Key Terms 119 ? Problems and Exercises 119 ? Case: Performance Lawn Equipment 124

Chapter 5: Probability Distributions and Data Modeling 125

Learning Objectives 125

Basic Concepts of Probability 126

Probability Rules and Formulas 128 ? Conditional Probability 129

Random Variables and Probability Distributions 132

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Discrete Probability Distributions 135

Expected Value of a Discrete Random Variable 136 ? Using Expected'

Value in Making Decisions 137 ? Variance of a Discrete Random

Variable 139 ? Bernoulli Distribution 139 ? Binomial Distribution 140

Poisson Distribution 142

Continuous Probability Distributions 143

Properties of Probability Density Functions 145 ? Uniform Distribution 146

Normal Distribution 148 ? The NORM.INV Function 150 ? Standard

Contents

vii

Normal Distribution 150 ? Using Standard Normal Distribution Tables 152 ? Exponential Distribution 152 ? Other Useful Distributions 154 ? Continuous Distributions 154 Random Sampling from Probability Distributions 155 Sampling from Discrete Probability Distributions 156 ? Sampling from Common Probability Distributions 157 "' Risk Solver Platform Distribution Functions 160 Data Modeling and Distribution Fitting 162 Goodness of Fit 164 p Distribution Fitting with Risk Solver Platform 164

Key Terms 166 ? Problems and Exercises 167 ? Case: Performance Lawn Equipment 174

Chapter 6: Sampling and Estimation 175

Learning Objectives 175 Statistical Sampling 176

Sampling Methods 176 Estimating Population Parameters 180

Unbiased Estimators 180 ? Errors in Point Estimation 181 Sampling Error 181

Understanding Sampling Error 181 Sampling Distributions 183

Sampling Distribution of the Mean 183 ? Applying the Sampling Distribution of the Mean 184 Interval Estimates 185 Confidence Intervals 186 Confidence Interval for the Mean with Known Population Standard Deviation 186 ? The f-Distribution 188 ? Confidence Interval for the Mean with Unknown Population Standard Deviation 189 ? Confidence Interval for a Proportion 189 ? Additional Types of Confidence Intervals 190 Using Confidence Intervals for Decision Making 192 Prediction Intervals 192 Confidence Intervals and Sample Size 193

Key Terms 195 ? Problems and Exercises 195 ? Case: Performance Lawn Equipment 198

Chapter 7: Statistical Inference 199

Learning Objectives 199 Hypothesis Testing 200

Hypothesis-Testing Procedure 201 One-Sample Hypothesis Tests 201

Understanding Risk in Hypothesis Testing 202 ? Selecting the Test Statistic 203 ? Drawing a Conclusion 204 ? p-Values 206 ? Two-Tailed Test of Hypothesis for the Mean 206 ? One-Sample Tests for Proportions 207 Two-Sample Hypothesis Tests 208 Two-Sample Tests for Differences in Means 209 ' ? Two-Sample Test'for Means with Paired Samples 212 ? Test for Equality of Variances 214

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Contents

Analysis of Variance 215 Assumptions of ANOVA 216

Chi-Square Test for Independence 217

Key Terms 221 ? Problems and Exercises 221 ? Case: Performance Lawn Equipment 225

Part 3: Predictive Analytics

Chapter 8: Predictive Modeling and Analysis 226

Learning Objectives 226 Logic-Driven Modeling 227

Strategies for Building Predictive Models 227 ? Data and Models 229 ? Models Involving Multiple Time Periods 231 ? Single-Period Purchase Decisions 231 ? Overbooking Decisions 233 ? Model Assumptions, Complexity, and Realism 234 Data-Driven Modeling 236 Retail Pricing Markdowns 238 ? Modeling Relationships and Trends in Data 238 Analyzing Uncertainty and Model Assumptions 243 What-If Analysis 244 ? Data Tables 244 ? Scenario Manager 248 ? Goal Seek 251 Model Analysis Using Risk Solver Platform 251 Parametric Sensitivity Analysis 251 ? Tornado Charts 255

Key Terms 256 ? Problems and Exercises 256 ? Case: Performance Lawn Equipment 260

Chapter 9: Regression Analysis 261

Learning Objectives 261

Simple Linear Regression 262

Finding the Best-Fitting Regression Line 263 ? Least-Squares Regression 265

Simple Linear Regression with Excel 267 ? Regression as Analysis of

Variance 269 ? Testing Hypotheses for Regression Coefficients 270 ?

Confidence Intervals for Regression Coefficients 271

Residual Analysis and Regression Assumptions 271

Checking Assumptions 272

Multiple Linear Regression 274

Building Good Regression Models 279

Correlation and Multicollinearity 281

Regression with Categorical Independent Variables 283

Categorical Variables with More Than Two Levels 285

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Regression Models with Nonlinear Terms 287

Key Terms 291 ? Problems and Exercises 291 ? Case: Performance Lawn Equipment 295

Chapter 10: Forecasting Techniques 297

Learning Objectives 297 Qualitative and Judgmental Forecasting 298

Historical Analogy 298 ? The Delphi Method 299 ? Indicators and Indexes 299

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