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.
1. Why Study Statistics?
Decision Making in an Uncertain Environment. Statistical Thinking. Journey to Making Decisions.2. Describing Data.
Classification of Variables. Tables and Graphs for Numerical Variables. Tables and Graphs for Categorical Variables. Measures of Central Tendency. Measures of Variability. Numerical Summary of Grouped Data.3. Summarizing Descriptive Relationships.
Scatter Plots. Covariance and Correlation. Obtaining Linear Relationships.4. Probability.
Random Experiment, Outcomes, Events. Probability and Its Postulates. Probability Rules. Bivariate Probabilities. Bayes' Theorem.5. Discrete Random Variables and Probability Distributions.
Random Variables. Probability Distributions for Discrete Random Variables. Expected Values and Variances for Discrete Random Variables. Binomial Distribution. Hypergeometric Distribution. Poisson Distribution. Jointly Distributed Discrete Random Variables.6. Continuous Random Variables and Probability Distributions.
Continuous Random Variables. Expected Values and Variances for Continuous Random Variables. Normal Distribution. Normal Approximation for Binomial. Exponential Distribution. Jointly Distributed Continuous Random Variables.7. Sampling and Sampling Distributions.
Sampling from a Population. Sampling Distribution of the Sample Mean. Sampling Distribution of a Sample Proportion. Sampling Distribution of the Sample Variance.8. Estimation.
Point Estimators. Confidence Intervals for the Mean of a Normal Distribution: Population Variance Known. Confidence Intervals for the Mean of a Normal Distribution: Population Variance Unknown. Confidence Intervals for Population Proportion (Large Samples). Confidence Intervals for Variance of a Normal Distribution. Confidence Intervals for the Difference between Two Population Means. Confidence Intervals for the Difference between Two Population Proportions (Large Samples). Sample Size Determination.9. Hypothesis Testing.
Concepts of Hypothesis Testing. Tests for the Mean of a Normal Distribution: Population Variance Known. Tests for the Mean of a Normal Distribution: Population Variance Unknown. Tests for the Population Proportion (Large Samples). Tests for the Variance of a Normal Distribution. Tests for the Difference between Two Population Means. Tests for the Difference between Two Population Proportions (Large Samples). Testing for the Equality of the Variances between Two Normal Populations. Assessing the Power of a Test. Some Comments on Hypothesis Testing.10. Simple Regression.
Correlation Analysis. Linear Regression Model. Least Squares Coefficient Estimators. Explanatory Power of a Linear Regression Equation. Statistical Inference: Hypothesis Tests and Confidence Intervals. Prediction. Graphical Analysis.11. Multiple Regression.
The Multiple Regression Model. Estimation of Coefficients. The Explanatory Power of a Multiple Regression. Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients. Tests on Sets on Regression Parameters. Prediction. Transformations for Non-Linear Regression Models. Dummy Variables for Regression Models. Multiple Regression Analysis Application Procedure.12. Additional Topics in Regression Analysis.
Model Building Methodology. Dummy Variables and Experimental Design. Lagged Dependent Variables. Specification Bias. Multicolinearity. Heteroscedasticity. Autocorrelations.13. Nonparametric Statistics.
Sign Test and Confidence Interval. Wilcoxon Signed Rank Test. Mann-Whitney U Test. Wilcoxon Rank Sum Test. Spearman Rank Correlation.14. Goodness-of-Fit Tests and Contingency Tables.
Goodness-of-Fit Tests: Specified Probabilities. Goodness-of-Fit Tests: Population Parameters Unknown. Contingency Tables.15. Analysis of Variance.
Comparison of Several Population Means. One-Way Analysis of Variance. The Kruskal-Wallis Test. Two-Way Analysis of Variance: One Observation Per Cell, Randomized Blocks. Two-Way Analysis of Variance: More Than One Observation Per Cell.16. Introduction to Quality.
The Importance of Quality. Control Charts for Means and Standard Deviations. Process Capability. Control Charts for Proportions. Control Charts for Number Occurrences. Computer Applications.17. Time Series Analysis and Forecasting.
Index Numbers. A Nonparamentric Test for Randomness. Components of a Time Series. Moving Averages. Exponential Smoothing. Autoregressive Models. Autoregressive Integrated Moving Average Models.18. Sample Size Determination.
Basic Steps of a Sampling Study. Sampling and Nonsampling Errors. Simple Random Sampling. Stratified Sampling. Determining Sample Size. Other Sampling Methods.19. Statistical Decision Theory.
Decision Making Under Uncertainty. Solutions Not Involving Specification of Probabilities: Maximin Criterion, Minimax Regret Criterion. Expected Monetary Value; TreePlan. Sampling Information: Bayesian Analysis and Value. Allowing for Risk: Utility Analysis.Appendix Tables.
Standard Normal Distribution Table. Binomial Probabilities. Values of e
-...`l. Poisson Probabilities. Chi-Square Distribution for Selected Probabilities. Student's t
Distribution for Selected Probabilities. F
Distribution for Selected Probabilities. Cutoff Points for the Distribution of the Wilcoxon Test Statistic. Cutoff Points for the Distribution of Spearman Rank Correlation Coefficient. Cutoff Points for the Distribution of the Durbin-Watson Test Statistic. Factors for the Control Charts.Answers to Selected Even-Numbers Exercises. Index.