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 Histograms 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
Three-Dimensional Graphing 484
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
Quadratic Transformations 519
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