Biostatistics is quickly becoming one of the most important areas of statistics due to the tremendous increase in health care needs. This book successfully introduces the terminology, concepts, and correct uses and interpretation of biostatistics. It is ideal for practitioners as well as students going into health care fields. Pedagogical features include formulas highlighted in text boxes and chapter summaries that highlight key vocabulary and concepts for the chapter. An accompanying Web site provides both MINITAB (R) and Microsoft (R) Office Excel (R) data files data for the case studies and exercises that are contained in the text.
Richard J. Rossi, PhD, is Professor and Head of the Department of Mathematical Sciences at Montana Tech of the University of Montana. He has previously served as president of the Montana Chapter of the American Statistical Association (1996 and 2001) and as associate editor for Biometrics. Dr. Rossi has published journal articles in his areas of research interest, which include nonparametric density estimation, finite mixture models, and computational statistics.?He is the author of Theorems, Corollaries, Lemmas, and Methods of Proof, also published by Wiley.
Preface. Chapter 1 Introduction to Biostatistics. 1.1 What is Biostatistics? 1.2 Populations, Samples, and Statistics. 1.3 Clinical Trials. 1.4 Data Set Descriptions. Glossary. Exercises. Chapter 2 Describing Populations. 2.1 Populations and Variables. 2.2 Population Distributions and Parameters. 2.3 Probability. 2.4 Probability Models. Glossary. Exercises. Chapter 3 Random Sampling. 3.1 Obtaining Representative Data. 3.2 Commonly Used Sampling Plans. 3.3 Determining the Sample Size. Glossary. Exercises. Chapter 4 Summarizing Random Samples. 4.1 Samples and Inferential Statistics. 4.2 Inferential Graphical Statistics. 4.3 Numerical Statistics for Univariate Datasets. 4.4 Statistics for Multivariate Data Sets. Glossary. Exercises. Chapter 5 Measuring the Reliability of Statistics. 5.1 Sampling Distributions. 5.2 The Sampling Distribution of a Sample Proportion. 5.3 The Sampling Distribution of x . 5.4 Comparisons Based on Two Samples. 5.5 Bootstrapping the Sampling Distribution of a Statistic. Glossary. Exercises. Chapter 6 Confidence Intervals. 6.1 Interval Estimation. 6.2 Confidence Intervals. 6.3 Single Sample Confidence Intervals. 6.4 Bootstrap Confidence Intervals. 6.5 Two Sample Comparative Confidence Intervals. Glossary. Exercises. Chapter 7 Testing Statistical Hypotheses. 7.1 Hypothesis Testing. 7.2 Testing Hypotheses about Proportions. 7.3 Testing Hypotheses about Means. 7.4 Some Final Comments on Hypothesis Testing. Glossary. Exercises. Chapter 8 Simple Linear Regression. 8.1 Bivariate Data, Scatterplots, and Correlation. 8.2 The Simple Linear Regression Model. 8.3 Fitting a Simple Linear Regression Model. 8.4 Assessing the Assumptions and Fit of a Simple Linear Regression Model. 8.5 Statistical Inferences based on a Fitted Model. 8.6 Inferences about the Response Variable. 8.7 Some Final Comments on Simple Linear Regression. Glossary. Exercises. Chapter 9 Multiple Regression. 9.1 Investigating Multivariate Relationships. 9.2 The Multiple Linear Regression Model. 9.3 Fitting a Multiple Linear Regression Model. 9.4 Assessing the Assumptions of a Multiple Linear Regression Model. 9.5 Assessing the Adequacy of Fit of a Multiple Regression Model. 9.6 Statistical Inferences Based Multiple Regression Model. 9.7 Comparing Multiple Regression Models. 9.8 Multiple Regression Models with Categorical Variables. 9.9 Variable Selection Techniques. 9.10 Some Final Comments on Multiple Regression. Glossary. Exercises. Chapter 10 Logistic Regression. 10.1 Odds and Odds Ratios. 10.2 The Logistic Regression Model. 10.3 Fitting a Logistic Regression Model. 10.4 Assessing the Fit of a Logistic Regression Model. 10.5 Statistical Inferences Based on a Logistic Regression Model. 10.6 Variable Selection. 10.7 Some Final Comments on Logistic Regression. Glossary. Exercises. Chapter 11 Design of Experiments. 11.1 Experiments versus Observational Studies. 11.2 The Basic Principles of Experimental Design. 11.3 Experimental Designs. 11.4 Factorial Experiments. 11.5 Models for Designed Experiments. 11.6 Some Final Comments of Designed Experiments. Glossary. Exercises. Chapter 12 Analysis of Variance. 12.1 Single-Factor Analysis of Variance. 12.2 Randomized Block Analysis of Variance. 12.3 Multifactor Analysis of Variance. 12.4 Selecting the Number of Replicates in Analysis of Variance. 12.5 Some Final Comments on Analysis of Variance. Glossary. Exercises. Chapter 13 Survival Analysis. 13.1 The Kaplan Meier Estimate of the Survival Function. 13.2 The Proportional Hazards Model. 13.3 Logistic Regression and Survival Analysis. 13.4 Some Final Comments on Survival Analysis. Glossary. Exercises. References. Appendix A. Problem Solutions. Index.