Statistics for business and economics

Look for statistics courses found within Economics, Business, Marketing, or statistics departments that are required for the Economics or Business major. Newbolds strength has been its unerring accuracy and statistical precision. It is also at a mathematically higher level than most business statistics texts. The new edition focuses on maintaining the statistical integrity of past editions while modernizing the text by integrating the use of statistical software, adding new examples and exercises (many with real data), and an emphasis on data analysis and interpretation of output. The new edition features both Excel and Minitab. *NEW-Emphasis on data analysis, use of software, and the interpretation of the computer output-The role of computers and statistical software has been thoroughly integrated in the text with an emphasis on interpreting computer output and data analysis. Within the chapters you will now find coverage of Minitab, Microsoft Excel and Prentice Halls PHStat, our statistical add-in for Excel. *NEW-Earlier introduction of key topics-Includes: introduction of statistical thinking (Ch. 1), introduction of processes and systems (Ch. 1), and coverage of bivariate data,

Author Notes

Bill Carlson is Professor of Economics and Department Chair of the Economics Department at St Olaf College, where he has taught for 29 years. His education includes engineering degrees from Michigan Technological University (BS) and Illinois Institute of Technology (MS) and a Ph.D. in Quantitative Management from the University of Michigan. His research includes numerous studies related to highway safety, management problems, and statistical education. He has previously published two statistics textbooks. He has led numerous student groups to study in various countries. He enjoys grandchildren, woodworking, travel, reading, and being on assignment in northern Wisconsin.

Betty Thorne. Author, researcher and award-winning teacher, Betty Thorne is Professor and Chair of the Department of Decision and Information Sciences in the School of Business Administration at Stetson University in DeLand, Florida. Winner of Stetson University's McEniry Award for Excellence in Teaching, the highest honor given to a Stetson University faculty member, Dr. Thorne also is the recipient of the Outstanding Teacher of the Year Award and Professor of the Year Award in the School of Business Administration at Stetson. She received her Bachelor of Science degree from Geneva College and the Master of Arts and Ph.D. degrees from Indiana University. She co-authored Applied Statistical Methods for Business, Economics and the Social Sciences (Prentice Hall, 1997) with Bill Carlson. Dr. Thorne is a member of the planning committee and serves as Secretary/Treasurer of the Making Statistics More Effective in Schools and Business conferences where she meets annually with fellow statisticians to discuss research and teaching issues. She also is a member of Decision Sciences Institute, the American Society for Quality, and the American Statistical Association.

She and her husband, Jim, have four children. They travel extensively, enjoy cruising, attend theological classes, and participate in international organizations dedicated to helping disadvantaged children.

Preface	p. xiii
Chapter 1 Why Study Statistics?	p. 1
1.1 Decision Making in an Uncertain Environment	p. 2
1.2 Statistical Thinking	p. 5
1.3 Journey to Making Decisions	p. 7
Chapter 2 Describing Data	p. 11
2.1 Classification of Variables	p. 12
2.2 Tables and Graphs for Numerical Data	p. 14
2.3 Tables and Graphs for Categorical Variables	p. 23
2.4 Measures of Central Tendency	p. 31
2.5 Measures of Variability	p. 38
2.6 Numerical Summary of Grouped Data	p. 47
Chapter 3 Summarizing Descriptive Relationships	p. 55
3.1 Scatter Plots	p. 56
3.2 Covariance and Correlation Coefficient	p. 60
3.3 Obtaining Linear Relationships	p. 65
3.4 Cross Tables	p. 70
Chapter 4 Probability	p. 79
4.1 Random Experiment, Outcomes, Events	p. 80
4.2 Probability and Its Postulates	p. 88
4.3 Probability Rules	p. 96
4.4 Bivariate Probabilities	p. 106
4.5 Bayes' Theorem	p. 116
Chapter 5 Discrete Random Variables and Probability Distributions	p. 129
5.1 Random Variables	p. 130
5.2 Probability Distributions for Discrete Random Variables	p. 132
5.3 Descriptive Measures for Discrete Random Variables	p. 134
5.4 Binomial Distribution	p. 144
5.5 Hypergeometric Distribution	p. 153
5.6 The Poisson Probability Distribution	p. 156
5.7 Jointly Distributed Discrete Random Variables	p. 160
Chapter 6 Continuous Random Variables and Probability Distributions	p. 179
6.1 Continuous Random Variables	p. 180
6.2 Expectations for Continuous Random Variables	p. 184
6.3 The Normal Distribution	p. 187
6.4 Normal Distribution Approximation for Binomial Distribution	p. 199
6.5 The Exponential Distribution	p. 204
6.6 Jointly Distributed Continuous Random Variables	p. 206
Chapter 7 Sampling and Sampling Distributions	p. 217
7.1 Sampling from a Population	p. 218
7.2 Sampling Distribution of the Sample Mean	p. 221
7.3 Sampling Distribution of a Sample Proportion	p. 235
7.4 Sampling Distribution of the Sample Variance	p. 240
Chapter 8 Estimation	p. 255
8.1 Point Estimators	p. 256
8.2 Confidence Intervals for the Mean of a Normal Distribution: Population Variance Known	p. 261
8.3 Confidence Intervals for the Mean of a Normal Distribution: Population Variance Unknown	p. 269
8.4 Confidence Intervals for Population Proportion (Large Samples)	p. 275
8.5 Confidence Intervals for Variance of a Normal Distribution	p. 280
8.6 Confidence Intervals for the Difference Between Means of Two Normal Populations	p. 283
8.7 Confidence Intervals for the Difference Between Two Population Proportions (Large Samples)	p. 293
8.8 Sample Size Determination	p. 296
Chapter 9 Hypothesis Testing	p. 305
9.1 Concepts of Hypothesis Testing	p. 306
9.2 Tests of the Mean of a Normal Distribution: Population Variance Known	p. 312
9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown	p. 323
9.4 Tests for the Population Proportion (Large Samples)	p. 327
9.5 Tests of the Variance of a Normal Distribution	p. 330
9.6 Tests for the Difference Between Two Population Means	p. 334
9.7 Tests for the Difference Between Two Population Proportions (Large Samples)	p. 346
9.8 Testing of the Equality of the Variances Between Two Normally Distributed Populations	p. 350
9.9 Assessing the Power of a Test	p. 354
9.10 Some Comments on Hypothesis Testing	p. 361
Chapter 10 Simple Regression	p. 369
10.1 Correlation Analysis	p. 370
10.2 Linear Regression Model	p. 374
10.3 Least Squares Coefficient Estimators	p. 379
10.4 The Explanatory Power of a Linear Regression Equation	p. 384
10.5 Statistical Inference: Hypothesis Tests and Confidence Intervals	p. 390
10.6 Prediction	p. 398
10.7 Graphical Analysis	p. 404
Chapter 11 Multiple Regression	p. 413
11.1 The Multiple Regression Model	p. 414
11.2 Estimation of Coefficients	p. 421
11.3 Explanatory Power of a Multiple Regression Equation	p. 426
11.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients	p. 432
11.5 Tests on Sets of Regression Parameters	p. 443
11.6 Prediction	p. 448
11.7 Transformations for Nonlinear Regression Models	p. 450
11.8 Dummy Variables for Regression Models	p. 459
11.9 Multiple Regression Analysis Application Procedure	p. 466
Chapter 12 Additional Topics in Regression Analysis	p. 485
12.1 Model-Building Methodology	p. 486
12.2 Dummy Variables and Experimental Design	p. 489
12.3 Lagged Values of the Dependent Variables as Regressors	p. 497
12.4 Specification Bias	p. 502
12.5 Multicollinearity	p. 505
12.6 Heteroscedasticity	p. 508
12.7 Autocorrelated Errors	p. 513
Chapter 13 Nonparametric Statistics	p. 531
13.1 Sign Test and Confidence Interval	p. 532
13.2 Wilcoxon Signed Rank Test	p. 539
13.3 Mann-Whitney U Test	p. 543
13.4 Wilcoxon Rank Sum Test	p. 547
13.5 Spearman Rank Correlation	p. 551
Chapter 14 Goodness-of-Fit Tests and Contingency Tables	p. 557
14.1 Goodness-of-Fit Tests: Specified Probabilities	p. 558
14.2 Goodness-of-Fit Tests: Population Parameters Unknown	p. 562
14.3 Contingency Tables	p. 566
Chapter 15 Analysis of Variance	p. 579
15.1 Comparison of Several Population Means	p. 580
15.2 One-Way Analysis of Variance	p. 582
15.3 The Kruskal-Wallis Test	p. 594
15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks	p. 596
15.5 Two-Way Analysis of Variance: More Than One Observation per Cell	p. 606
Chapter 16 Introduction to Quality	p. 621
16.1 The Importance of Quality	p. 622
16.2 Control Charts for Means and Standard Deviations	p. 626
16.3 Process Capability	p. 636
16.4 Control Chart for Proportions	p. 638
16.5 Control Charts for Number of Occurrences	p. 642
16.6 Computer Applications	p. 645
Chapter 17 Time Series Analysis and Forecasting	p. 655
17.1 Index Numbers	p. 657
17.2 A Nonparametric Test for Randomness	p. 665
17.3 Components of a Time Series	p. 668
17.4 Moving Averages	p. 671
17.5 Exponential Smoothing	p. 679
17.6 Autoregressive Models	p. 690
17.7 Autoregressive Integrated Moving Average Models	p. 696
Chapter 18 Additional Topics in Sampling	p. 699
18.1 Basic Steps of a Sampling Study	p. 700
18.2 Sampling and Nonsampling Errors	p. 705
18.3 Simple Random Sampling	p. 706
18.4 Stratified Sampling	p. 712
18.5 Determining Sample Size	p. 723
18.6 Other Sampling Methods	p. 728
Chapter 19 Statistical Decision Theory	p. 739
19.1 Decision Making Under Uncertainty	p. 740
19.2 Solutions Not Involving Specification of Probabilities: Maximin Criterion, Minimax Regret Criterion	p. 743
19.3 Expected Monetary Value: TreePlan	p. 748
19.4 Sample Information: Bayesian Analysis and Value	p. 758
19.5 Allowing for Risk: Utility Analysis	p. 771
Appendix Tables
1. Cumulative Distribution Function of the Standard Normal Distribution	p. 780
2. Probability Function of the Binomial Distribution	p. 782
3. Cumulative Binomial Probabilities	p. 787
4. Values of e-[superscript lambda]	p. 792
5. Individual Poisson Probabilities	p. 793
6. Cumulative Poisson Probabilities	p. 801
7. Cutoff Points of the Chi-Square Distribution Function	p. 810
8. Cutoff Points for the Student's t Distribution	p. 811
9. Cutoff Points for the F Distribution	p. 812
10. Cutoff Points for the Distribution of the Wilcoxon Test Statistic	p. 814
11. Cutoff Points for the Distribution of Spearman Rank Correlation Coefficient	p. 815
12. Cutoff Points for the Distribution of the Durbin-Watson Test Statistic	p. 816
13. Factors for Control Charts	p. 818
14. Cumulative Distribution Function of the Runs Test Statistic	p. 819
Answers to Selected Even-Numbered Exercises	p. 821
Index	p. 1

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