# Statistics for Business and Economics, 10th Edition PDF by Paul Newbold, William L Carlson, St Olaf College and Betty M Thorne

## Statistics for Business and Economics, Tenth Global Edition

By Paul Newbold, William L. Carlson, St. Olaf College and Betty M. Thorne

Contents:

Preface 13

Data File Index 23

CHAPTER 1 Describing Data: Graphical 25

1.1 Decision Making in an Uncertain Environment 26

Random and Systematic Sampling 26

Sampling and Nonsampling Errors 28

1.2 Classification of Variables 29

Categorical and Numerical Variables 29

Measurement Levels 30

1.3 Graphs to Describe Categorical Variables 32

Tables and Charts 32

Cross Tables 33

Pie Charts 35

Pareto Diagrams 36

1.4 Graphs to Describe Time-Series Data 39

1.5 Graphs to Describe Numerical Variables 44

Frequency Distributions 44

Histograms and Ogives 48

Shape of a Distribution 48

Stem-and-Leaf Displays 50

Scatter Plots 51

1.6 Data Presentation Errors 55

Misleading Time-Series Plots 57

CHAPTER 2 Describing Data: Numerical 63

2.1 Measures of Central Tendency and Location 63

Mean, Median, and Mode 64

Shape of a Distribution 66

Geometric Mean 67

Percentiles and Quartiles 68

2.2 Measures of Variability 72

Range and Interquartile Range 73

Box-and-Whisker Plots 73

Variance and Standard Deviation 75

Coefficient of Variation 79

Chebyshev’s Theorem and the Empirical Rule 79

z-Score 81

2.3 Weighted Mean and Measures of Grouped Data 84

2.4 Measures of Relationships Between Variables 88

Case Study: Mortgage Portfolio 95

CHAPTER 3 Probability 97

3.1 Random Experiment, Outcomes, and Events 98

3.2 Probability and Its Postulates 105

Classical Probability 105

Permutations and Combinations 106

Relative Frequency 110

Subjective Probability 111

3.3 Probability Rules 115

Conditional Probability 117

Statistical Independence 120

3.4 Bivariate Probabilities 126

Odds 130

Overinvolvement Ratios 130

3.5 Bayes’ Theorem 136

Subjective Probabilities in Management Decision Making 142

CHAPTER 4 Discrete Random Variables and Probability Distributions 150

4.1 Random Variables 151

4.2 Probability Distributions for Discrete Random Variables 152

4.3 Properties of Discrete Random Variables 156

Expected Value of a Discrete Random Variable 156

Variance of a Discrete Random Variable 157

Mean and Variance of Linear Functions of a Random Variable 159

4.4 Binomial Distribution 163

Developing the Binomial Distribution 164

4.5 Poisson Distribution 171

Poisson Approximation to the Binomial Distribution 175

Comparison of the Poisson and Binomial Distributions 176

4.6 Hypergeometric Distribution 177

4.7 Jointly Distributed Discrete Random Variables 180

Conditional Mean and Variance 184

Computer Applications 184

Linear Functions of Random Variables 184

Covariance 185

Correlation 186

Portfolio Analysis 190

CHAPTER 5 Continuous Random Variables and Probability Distributions 201

5.1 Continuous Random Variables 202

The Uniform Distribution 205

5.2 Expectations for Continuous Random Variables 207

5.3 The Normal Distribution 210

Normal Probability Plots 219

5.4 Normal Distribution Approximation for Binomial Distribution 223

Proportion Random Variable 227

5.5 The Exponential Distribution 229

5.6 Jointly Distributed Continuous Random Variables 232

Linear Combinations of Random Variables 236

Financial Investment Portfolios 236

Cautions Concerning Finance Models 240

CHAPTER 6 Sampling and Sampling Distributions 248

6.1 Sampling from a Population 249

Development of a Sampling Distribution 250

6.2 Sampling Distributions of Sample Means 253

Central Limit Theorem 258

Monte Carlo Simulations: Central Limit Theorem 258

Acceptance Intervals 264

6.3 Sampling Distributions of Sample Proportions 269

6.4 Sampling Distributions of Sample Variances 274

CHAPTER 7 Estimation: Single Population 288

7.1 Properties of Point Estimators 289 Unbiased 290

Most Efficient 291

7.2 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Known 295

Intervals Based on the Normal Distribution 296

Reducing Margin of Error 299

7.3 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Unknown 301

Student’s t Distribution 301

Intervals Based on the Student’s t Distribution 303

7.4 Confidence Interval Estimation for Population Proportion (Large Samples) 307

7.5 Confidence Interval Estimation for the Variance of a Normal Distribution 310

7.6 Confidence Interval Estimation: Finite Populations 313

Population Mean and Population Total 313

Population Proportion 316

7.7 Sample-Size Determination: Large Populations 319

Mean of a Normally Distributed Population, Known Population Variance 319

Population Proportion 321

7.8 Sample-Size Determination: Finite Populations 323

Sample Sizes for Simple Random Sampling: Estimation of the Population Mean or Total 324

Sample Sizes for Simple Random Sampling: Estimation of Population Proportion 325

CHAPTER 8 Estimation: Additional Topics 332

8.1 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Dependent Samples 333

8.2 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Independent Samples 337

Two Means, Independent Samples, and Known Population Variances 337

Two Means, Independent Samples, and

Unknown Population Variances Assumed to Be Equal 339

Two Means, Independent Samples, and Unknown Population Variances Not Assumed to Be Equal 341

8.3 Confidence Interval Estimation of the Difference Between Two Population Proportions (Large Samples) 344

CHAPTER 9 Hypothesis Testing: Single Population 350

9.1 Concepts of Hypothesis Testing 351

9.2 Tests of the Mean of a Normal Distribution: Population Variance Known 356

p-Value 358

Two-Sided Alternative Hypothesis 364

9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown 366

9.4 Tests of the Population Proportion (Large Samples) 370

9.5 Assessing the Power of a Test 372

Tests of the Mean of a Normal Distribution: Population Variance Known 373

Power of Population Proportion Tests (Large Samples) 375

9.6 Tests of the Variance of a Normal Distribution 379

CHAPTER 10 Hypothesis Testing: Additional Topics 389

10.1 Tests of the Difference Between Two

Normal Population Means: Dependent Samples 391

Two Means, Matched Pairs 391

10.2 Tests of the Difference Between Two Normal Population Means: Independent Samples 395

Two Means, Independent Samples, Known Population Variances 395

Two Means, Independent Samples, Unknown Population Variances Assumed to Be Equal 397

Two Means, Independent Samples, Unknown Population Variances Not Assumed to Be Equal 400

10.3 Tests of the Difference Between Two Population Proportions (Large Samples) 403

10.4 Tests of the Equality of the Variances Between Two Normally Distributed Populations 407

10.5 Some Comments on Hypothesis Testing 410

CHAPTER 11 Simple Regression 421

11.1 Overview of Linear Models 422

11.2 Linear Regression Model 425

11.3 Least Squares Coefficient Estimators 431

Computer Computation of Regression Coefficients 433

11.4 The Explanatory Power of a Linear Regression Equation 435

Coefficient of Determination, R2 437

11.5 Statistical Inference: Hypothesis Tests and Confidence Intervals 442

Hypothesis Test for Population Slope Coefficient Using the F Distribution 447

11.6 Prediction 450

11.7 Correlation Analysis 456

Hypothesis Test for Correlation 456

11.8 Beta Measure of Financial Risk 460

11.9 Graphical Analysis 462

CHAPTER 12 Multiple Regression 477

12.1 The Multiple Regression Model 478

Model Specification 478

Model Objectives 480

Model Development 481

12.2 Estimation of Coefficients 485

Least Squares Procedure 486

12.3 Explanatory Power of a Multiple Regression Equation 492

12.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients 497

Confidence Intervals 499

Tests of Hypotheses 501

12.5 Tests on Regression Coefficients 509

Tests on All Coefficients 509

Test on a Subset of Regression Coefficients 510

Comparison of F and t Tests 512

12.6 Prediction 515

12.7 Transformations for Nonlinear Regression Models 518

Logarithmic Transformations 521

12.8 Dummy Variables for Regression Models 526

Differences in Slope 529

12.9 Multiple Regression Analysis Application Procedure 533

Model Specification 533

Multiple Regression 535

Effect of Dropping a Statistically Significant Variable 536

Analysis of Residuals 538

CHAPTER 13 Additional Topics in Regression Analysis 555

13.1 Model-Building Methodology 556

Model Specification 556

Coefficient Estimation 557

Model Verification 558

Model Interpretation and Inference 558

13.2 Dummy Variables and Experimental Design 558

Experimental Design Models 562

Public Sector Applications 567

13.3 Lagged Values of the Dependent Variable as Regressors 571

13.4 Specification Bias 575

13.5 Multicollinearity 578

13.6 Heteroscedasticity 581

13.7 Autocorrelated Errors 586

Estimation of Regressions with Autocorrelated Errors 590

Autocorrelated Errors in Models with

Lagged Dependent Variables 594

CHAPTER 14 Analysis of Categorical Data 606

14.1 Goodness-of-Fit Tests: Specified Probabilities 607

14.2 Goodness-of-Fit Tests: Population Parameters Unknown 613

A Test for the Poisson Distribution 613

A Test for the Normal Distribution 615

14.3 Contingency Tables 618

14.4 Nonparametric Tests for Paired or Matched Samples 623

Sign Test for Paired or Matched Samples 623

Wilcoxon Signed Rank Test for Paired or Matched Samples 626

Normal Approximation to the Sign Test 627

Normal Approximation to the Wilcoxon Signed Rank Test 628

Sign Test for a Single Population Median 630

14.5 Nonparametric Tests for Independent

Random Samples 632

Mann-Whitney U Test 632

Wilcoxon Rank Sum Test 635

14.6 Spearman Rank Correlation 638

14.7 A Nonparametric Test for Randomness 640

Runs Test: Small Sample Size 640

Runs Test: Large Sample Size 642

CHAPTER 15 Analysis of Variance 649

15.1 Comparison of Several Population Means 649

15.2 One-Way Analysis of Variance 651

Multiple Comparisons Between Subgroup Means 658

Population Model for One-Way Analysis of Variance 659

15.3 The Kruskal-Wallis Test 662

15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks 665

15.5 Two-Way Analysis of Variance: More Than One Observation per Cell 674

CHAPTER 16 Time-Series Analysis and Forecasting 688

16.1 Components of a Time Series 689

16.2 Moving Averages 693

Extraction of the Seasonal Component Through Moving Averages 696

16.3 Exponential Smoothing 701

The Holt-Winters Exponential Smoothing Forecasting Model 704

Forecasting Seasonal Time Series 708

16.4 Autoregressive Models 712

16.5 Autoregressive Integrated Moving Average Models 717

CHAPTER 17 Additional Topics in Sampling 720

17.1 Stratified Sampling 720

Analysis of Results from Stratified Random Sampling 722

Allocation of Sample Effort Among Strata 727

Determining Sample Sizes for Stratified Random Sampling with Specified Degree of Precision 729

17.2 Other Sampling Methods 733

Cluster Sampling 733

Two-Phase Sampling 736

Nonprobabilistic Sampling Methods 738

APPENDIX TABLES 742

INDEX 787

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