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
The Addition Rule 114
The Addition Rule for Mutually
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
The Adjusted R2 419
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
Quadratic Regression Models 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