# Essentials of Business Statistics: Communicating with Numbers, 2nd Edition PDF by Sanjiv Jaggia and Alison Kelly

## Essentials of Business Statistics: Communicating with Numbers, 2nd Edition

By Sanjiv Jaggia and Alison Kelly Contents:

CHAPTER 1

Statistics And Data 2

1.1 The Relevance of Statistics 4

1.2 What is Statistics? 5

The Need for Sampling 6

Cross-Sectional and Time Series Data 6

Structured and Unstructured Data 7

Big Data 8

Data on the Web 8

1.3 Variables and Scales of Measurement 10

The Nominal Scale 11

The Ordinal Scale 12

The Interval Scale 13

The Ratio Scale 14

Synopsis of Introductory Case 15

Conceptual Review 16

CHAPTER 2

Tabular And Graphical Methods 18

2.1 Summarizing Qualitative Data 20

Pie Charts and Bar Charts 21

Cautionary Comments When Constructing or Interpreting Charts or Graphs 24

Using Excel to Construct a Pie Chart and a Bar Chart 24

A Pie Chart 24

A Bar Chart 25

2.2 Summarizing Quantitative Data 27

Guidelines for Constructing a Frequency Distribution 28

Synopsis Of Introductory Case 32

Histograms, Polygons, and Ogives 32

Using Excel to Construct a Histogram, a Polygon, and an Ogive 36

A Histogram Constructed from Raw Data 36

A Histogram Constructed from a Frequency Distribution 37

A Polygon 38

An Ogive 38

2.3 Stem-and-Leaf Diagrams 42

2.4 Scatterplots 44

Using Excel to Construct a Scatterplot 46

Writing with Statistics 47

Conceptual Review 49

Additional Exercises And Case Studies 50

Exercises 50

Case Studies 53

Appendix 2.1 Guidelines for Other Software Packages 55

CHAPTER 3

Numerical Descriptive Measures 60

3.1 Measures of Central Location 62

The Mean 62

The Median 64

The Mode 65

The Weighted Mean 66

Using Excel to Calculate Measures of

Central Location 67

Using Excel’s Function Option 67

Using Excel’s Data Analysis Toolpak Option 68

Note on Symmetry 69

3.2 Percentiles and Boxplots 71

Calculating the pth Percentile 72

Note on Calculating Percentiles 73

Constructing and Interpreting a Boxplot 73

3.3 Measures of Dispersion 76

Range 76

The Mean Absolute Deviation 77

The Variance and the Standard Deviation 78

The Coefficient of Variation 79

Using Excel to Calculate Measures of Dispersion 80

Using Excel’s Function Option 80

Using Excel’s Data Analysis Toolpak Option 80

3.4 Mean-Variance Analysis and the Sharpe Ratio 81

Synopsis of Introductory Case 83

3.5 Analysis of Relative Location 84

Chebyshev’s Theorem 85

The Empirical Rule 85

z-Scores 86

3.6 Summarizing Grouped Data 89

3.7 Measures of Association 92

Using Excel to Calculate Measures of Association 94

Writing with Statistics 95

Conceptual Review 97

Additional Exercises and Case Studies 98

Exercises 98

Case Studies 101

Appendix 3.1: Guidelines for Other Software Packages 102

CHAPTER 4

Introduction To Probability 104

4.1 Fundamental Probability Concepts 106

Events 107

Assigning Probabilities 109

4.2 Rules of Probability 113

The Complement Rule 113

Exclusive Events 115

Conditional Probability 116

Independent and Dependent Events 118

The Multiplication Rule 119

The Multiplication Rule for

Independent Events 119

4.3 Contingency Tables and Probabilities 123

A Note on Independence 126

Synopsis of Introductory Case 126

4.4 The Total Probability Rule and Bayes’ Theorem 128

The Total Probability Rule 128

Bayes’ Theorem 131

Writing With Statistics 135

Conceptual Review 137

Additional Exercises and Case Studies 138

Exercises 138

Case Studies 142

CHAPTER 5

Discrete Probability Distributions 144

5.1 Random Variables and Discrete Probability Distributions 146

The Discrete Probability Distribution 147

5.2 Expected Value, Variance, and Standard Deviation 151

Expected Value 152

Variance and Standard Deviation 152

Risk Neutrality and Risk Aversion 153

5.3 The Binomial Distribution 156

Using Excel to Obtain Binomial Probabilities 161

5.4 The Poisson Distribution 164

Synopsis of Introductory Case 167

Using Excel to Obtain Poisson Probabilities 167

5.5 The Hypergeometric Distribution 169

Using Excel to Obtain Hypergeometric Probabilities 171

Writing with Statistics 173

Conceptual Review 175

Additional Exercises and Case Studies 176

Exercises 176

Case Studies 178

Appendix 5.1: Guidelines for Other Software Packages 179

CHAPTER 6

Continuous Probability Distributions 182

6.1 Continuous Random Variables and the Uniform Distribution 184

The Continuous Uniform Distribution 185

6.2 The Normal Distribution 188

Characteristics of the Normal Distribution 189

The Standard Normal Distribution 190

Finding a Probability for a Given z Value 191

Finding a z Value for a Given Probability 193

The Transformation of Normal Random Variables 195

Synopsis of Introductory Case 199

A Note on the Normal Approximation of the Binomial Distribution 199

Using Excel for the Normal Distribution 199

6.3 The Exponential Distribution 204

Using Excel for the Exponential Distribution 207

Writing with Statistics 209

Conceptual Review 210

Additional Exercises and Case Studies 211

Exercises 211

Case Studies 214

Appendix 6.1: Guidelines for Other Software Packages 215

CHAPTER 7

Sampling And Sampling Distributions 218

7.1 Sampling 220

Classic Case of a “Bad” Sample: The Literary Digest Debacle of 1936 220

Trump’s Stunning Victory in 2016 221

Sampling Methods 222

Using Excel to Generate a Simple Random Sample 224

7.2 The Sampling Distribution of the Sample Mean 225

The Expected Value and the Standard Error of the Sample Mean 226

Sampling from a Normal Population 227

The Central Limit Theorem 228

7.3 The Sampling Distribution of the Sample Proportion 232

The Expected Value and the Standard Error of the Sample Proportion 232

Synopsis of Introductory Case 236

7.4 The Finite Population Correction Factor 237

7.5 Statistical Quality Control 240

Control Charts 241

Using Excel to Create a Control Chart 244

Writing with Statistics 247

Conceptual Review 248

Additional Exercises and Case Studies 250

Exercises 250

Case Studies 252

Appendix 7.1: Derivation of the Mean and the Variance for ¯X and ¯P 253

Appendix 7.2: Properties of Point Estimators 254

Appendix 7.3: Guidelines for Other Software Packages 255

CHAPTER 8

Interval Estimation 258

8.1 Confidence Interval for the Population Mean when σ is Known 260

Constructing a Confidence Interval for μ When σ Is Known 261

The Width of a Confidence Interval 263

Using Excel to Construct a Confidence Interval for μ When σ Is Known 265

8.2 Confidence Interval for the Population

Mean When σ is Unknown 268

The t Distribution 268

Summary of the tdf Distribution 268

Locating tdf Values and Probabilities 269

Constructing a Confidence Interval for μ When σ Is Unknown 270

Using Excel to Construct a Confidence Interval for μ When σ Is Unknown 271

8.3 Confidence Interval for the Population Proportion 275

8.4 Selecting the Required Sample Size 278

Selecting n to Estimate μ 279

Selecting n to Estimate p 280

Synopsis of Introductory Case 281

Writing with Statistics 282

Conceptual Review 284

Additional Exercises and Case Studies 285

Exercises 285

Case Studies 288

Appendix 8.1: Guidelines for Other Software Packages 290

CHAPTER 9

Hypothesis Testing 292

9.1 Introduction to Hypothesis Testing 294

The Decision to “Reject” or “Not Reject” the Null Hypothesis 294

Defining the Null and the Alternative Hypotheses 295

Type I and Type II Errors 297

9.2 Hypothesis Test for the Population Mean When σ is Known 300

The p-Value Approach 300

Confidence Intervals and Two-Tailed Hypothesis Tests 304

Using Excel to Test μ When σ Is Known 305

One Last Remark 306

9.3 Hypothesis Test for the Population Mean

When σ is Unknown 308

Using Excel to Test μ When σ is Unknown 309

Synopsis of Introductory Case 310

9.4 Hypothesis Test for the Population Proportion 313

Writing with Statistics 317

Conceptual Review 318

Additional Exercises and Case Studies 320

Exercises 320

Case Studies 322

Appendix 9.1: The Critical Value Approach 324

Appendix 9.2: Guidelines for Other Software Packages 326

CHAPTER 10

Comparisons Involving Means 328

10.1 Inference Concerning the Difference Between Two Means 330

Confidence Interval for μ1 − μ2 330

Hypothesis Test for μ1 − μ2 332

Using Excel for Testing Hypotheses about μ1 − μ2 334

10.2 Inference Concerning Mean Differences 340

Recognizing a Matched-Pairs Experiment 341

Confidence Interval for μD 341

Hypothesis Test for μD 342

Using Excel for Testing Hypotheses about μD 344

Synopsis of Introductory Case 345

10.3 Inference Concerning Differences Among Many Means 349

The F Distribution 349

Finding F (d f 1 ,d f 2 ) Values and Probabilities 349

One-Way ANOVA Test 350

Between-Treatments Estimate of σ2: MSTR 352

Within-Treatments Estimate of σ2: MSE 353

The One-Way ANOVA Table 355

Using Excel to Construct a One-Way ANOVA Table 355

Writing with Statistics 359

Conceptual Review 360

Additional Exercises and Case Studies 362

Exercises 362

Case Studies 366

Appendix 10.1: Guidelines for Other Software

Packages 367

CHAPTER 11

Comparisons Involving Proportions 370

11.1 Inference Concerning the Difference Between Two Proportions 372

Confidence Interval for p1 − p2 372

Hypothesis Test for p1 − p2 373

11.2 Goodness-Of-Fit Test for a Multinomial Experiment 378

The χ 2 Distribution 378

Finding χ df 2 Values and Probabilities 379

11.3 Chi-Square Test For Independence 385

Calculating Expected Frequencies 386

Synopsis of Introductory Case 389

Writing with Statistics 392

Conceptual Review 393

Additional Exercises and Case Studies 394

Exercises 394

Case Studies 398

Appendix 11.1: Guidelines for Other Software

Packages 399

CHAPTER 12

Basics Of Regression Analysis 402

12.1 The Simple Linear Regression Model 404

Determining the Sample Regression Equation 406

Using Excel 408

Constructing a Scatterplot with Trendline 408

Estimating a Simple Linear Regression Model 408

12.2 The Multiple Linear Regression Model 411

Using Excel to Estimate a Multiple Linear

Regression Model 413

12.3 Goodness-of-Fit Measures 416

The Standard Error of the Estimate 416

The Coefficient of Determination, R2 417

12.4 Tests of Significance 422

Tests of Individual Significance 422

A Test for a Nonzero Slope Coefficient 425

Test of Joint Significance 427

Reporting Regression Results 429

Synopsis of Introductory Case 429

12.5 Model Assumptions and Common Violations 433

Common Violation 1: Nonlinear Patterns 435

Detection 435

Remedy 436

Common Violation 2: Multicollinearity 436

Detection 437

Remedy 438

Common Violation 3: Changing Variability 438

Detection 438

Remedy 439

Common Violation 4: Correlated Observations 440

Detection 440

Remedy 441

Common Violation 5: Excluded Variables 441

Remedy 441

Summary 441

Using Excel to Construct Residual Plots 442

Writing with Statistics 444

Conceptual Review 446

Additional Exercises and Case Studies 448

Case Studies 451

Appendix 12.1: Guidelines for Other Software Packages 453

CHAPTER 13

More On Regression Analysis 456

13.1 Dummy Variables 458

A Qualitative Explanatory Variable with Two Categories 458

A Qualitative Explanatory Variable with Multiple Categories 461

13.2 Interactions with Dummy Variables 467

Synopsis of Introductory Case 471

13.3 Regression Models for Nonlinear Relationships 473

Regression Models with Logarithms 478

The Log-Log Model 478

The Logarithmic Model 479

The Exponential Model 480

13.4 Trend Forecasting Models 487

The Linear and the Exponential Trend 487

Polynomial Trends 490

13.5 Forecasting with Trend and Seasonality 495

Seasonal Dummy Variables 495

Writing with Statistics 499

Conceptual Review 501

Additional Exercises and Case Studies 503

Case Studies 507

Appendixes:

Appendix A Tables 510

Appendix B Answers to Selected Even-Numbered

Exercises 520

Glossary 537

Index I-1

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