Chapter 01: A Brief Overview of Statistics
1.1 What is Statistics? 3
1.2 Why study statistics? 6
1.3 Business and Statistics 8
1.4 Statistical Challenge 11
1.5 Critical Thinking 18
Summary 21
Unit Practice Problem 22

Chapter 02 Data Collection
2.1 Variables and Data 27
2.2 Measurement Level 31
2.3 Sampling Concept 36
2.4 Sampling Methods 40
2.5 Data Source 49
2.6 Survey 51
Summary 56
Unit Practice Problem 57

Chapter 03 Visually Describing Data
3.1 Stem-and-leaf plots and dot plots 63
3.2 Frequency distribution and histogram 68
3.3 Effective Excel Charts 77
3.4 Lead Table 78
3.5 Bar Chart 82
3.6 Pie Chart 86
3.7 Scatterplot 87
3.8 Table 91
3.9 Illusory Graph 95
Summary 100
Unit Practice Problem 101

Chapter 04 Descriptive Statistics
4.1 Numerical Skills 107
4.2 Center Measurement 110
4.3 Volatility Measurement 123
4.4 Standardized Data 132
4.5 Percentiles, Quartiles, and Boxplots 137
4.6 Covariance and Correlation 147
4.7 Group Data 151
4.8 Skewness and Kurtosis 154
Summary 158
Unit Practice Problem 162

Chapter 05 Probability
5.1 Probability Experiment 169
5.2 Probability 171
5.3 Rules of Probability 176
5.4 Independent incident 182
5.5 Partition Table 187
5.6 Tree Diagram 193
5.7 Bayes' Theorem 195
5.8 Counting 201
Summary 205
Unit Practice Problem 207

Chapter 06 Discrete Probability Distributions
6.1 Discrete Probability Distributions 213
6.2 Expected Value and Variance 216
6.3 Discrete Uniform Distribution 221
6.4 Binomial Distribution 223
6.5 Poisson Distribution 230
6.6 Hypergeometric Distribution 237
Summary 240
Unit Practice Problem 242

Chapter 07 Continuous Probability Distributions
7.1 Continuous Probability Distributions 247
7.2 Continuous Uniform Distribution 249
7.3 Normal Distribution 252
7.4 Standard Normal Distribution 255
7.5 Normal Distribution Approximation 269
7.6 Exponential Distribution 273
Summary 279
Unit Practice Problem 281

Chapter 08 Sampling Distribution and Estimation
8.1 Sampling and Estimation 287
8.2 Central Limit Theorem 292
8.3 Confidence Interval for Population Mean (μ) When σ is Known 296
8.4 Confidence interval for population mean (μ) when μ is unknown 300
8.5 Confidence Intervals for Population Proportions 307
8.6 Estimation from Finite Populations 314
8.7 Determining the Sample Size for Estimating the Population Mean 316
8.8 Determining the Sample Size for Population Proportion Estimation 318
Summary 321
Unit Practice Problem 323

Chapter 09 Single-Sample Hypothesis Testing
9.1 Principles of Hypothesis Testing 329
9.2 Type I and Type II Errors 333
9.3 Decision Rules and Thresholds 336
9.4 Hypothesis Testing for the Mean: When the Population Variance is Known 340
9.5 Hypothesis Testing for the Mean: When the Population Variance is Unknown 347
9.6 Test for the population ratio 353
Summary 362
Unit Practice Problem 363

Chapter 10 Two-Sample Hypothesis Testing
10.1 Two-Sample Hypothesis Testing 367
10.2 Comparing Two Means: Independent Samples 370
10.3 Confidence Interval for the Difference Between Two Means 379
10.4 Comparing Two Means: 380 for Paired Samples
10.5 Comparing Two Ratios 387
10.6 Confidence Interval for the Difference Between Two Proportions 392
10.7 Comparing Two Variances 394
Summary 401
Unit Practice Problem 403

Chapter 11 Analysis of Variance
11.1 Basic Concepts of ANOVA 411
11.2 One-Way ANOVA (Completely Randomized Model) 414
11.3 Multiple Comparison Test 422
11.4 Test for Equivalence of Variances 424
11.5 Unrepeated Two-Way ANOVA (Randomized Block Model) 427
11.6 Repeated Two-Way ANOVA (Full Factor Model) 434
Summary 443
Unit Practice Problem 444

Chapter 12 Simple Regression Model
12.1 Data Visualization and Correlation Analysis 451
12.2 Simple Regression Analysis 457
12.3 Regression Model 460
12.4 Ordinary Least Squares 464
12.5 Significance Test 470
12.6 Analysis of Variance: Overall Fit 476
Confidence and prediction intervals for 12.7 Y 480
12.8 Residual Test 483
12.9 Abnormal Observations 489
Summary 493
Unit Practice Problem 495

Chapter 13 Multiple Regression Models
13.1 Multiple Regression Model 505
13.2 Overall Fit Assessment 511
13.3 Significance of Predictor Variables 515
Confidence interval for 13.4 Y 519
13.5 Categorical Predictors 522
13.6 Testing for Nonlinear and Interaction Effects 532
13.7 Multicollinearity 536
13.8 Regression Model Diagnostics 540
13.9 Other Issues in Regression Models 547
Summary 550
Unit Practice Problem 552

Chapter 14 Chi-Square Test
14.1 Chi-Square Test for Independence 563
14.2 Chi-Square Test for Goodness of Fit 575
14.3 Uniform Distribution Goodness-of-Fit Test 579
14.4 Poisson Distribution Goodness-of-Fit Test 583
14.5 Normal Distribution Chi-Square Goodness-of-Fit Test 588
Summary 592
Unit Practice Problem 593

supplement
A binomial distribution table 598
B Poisson distribution table 600
C-1 Standard Normal Distribution Table 603
C-2 Cumulative Standard Normal Distribution 604
D Student's t-distribution critical value 606
E Chi-square distribution critical value 607
F F0.10 critical value 608
G Odd Numbers Practice Problems Answers 616
Answers to H Review Problem 643
I Excel Statistical Functions 646
How to Use JR and RStudio 650

Features of this book

Although there are already many introductory books on pure statistics, they are often limited in their application to economics or business management.
On the other hand, many statistics books in the economics field focus too much on regression analysis, making them overly difficult for students learning statistics for the first time.
In that respect, this book maintains a good balance and is therefore suitable for students who are just beginning to study statistics related to business and economics.
However, this does not mean that this book only contains basic information.
Although it focuses on application rather than theory, the scope of this book is quite broad.
Even if you have already had a brief introduction to statistics, this book will help you reorganize and systematize your statistical knowledge.
Another strength of this book is that it goes beyond simply explaining various concepts theoretically, and allows you to use software to produce practical results.
The statistical software used in this book is primarily Excel, but it also covers the use of various other software.
In particular, the 7th edition includes instructions for using R, a free software widely used for data analysis.

What's added or changed in the revised edition

The 7th edition has been improved upon previous editions, reflecting feedback from professors and students.
We've added a new section called "Analytics Application Cases" to introduce how statistics are being used to analyze real-world data and solve problems.
Additionally, we support the latest version of Excel to help students practice, and the Mini Cases in each chapter are updated with the latest examples from the fields of economics and business administration.