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Introductory text in statistics specifically aimed at students specialising in the life sciences. Its primary aims are to show students how statistical reasoning is relevant to biological, medical, and agricultural research; to enable students confidently to carry out simple statistical analyses and to interpret the results; to raise students' consciousness concerning basic statistical issues such as randomization, confounding, and the role of independent replication.
1. Introduction. Statistics and the Life Sciences. Examples and Overview. 2. Description of Populations and Samples. Introduction. Frequency Distributions: Techniques for Data. Frequency Distributions: Shapes and Examples. Descriptive Statistics: Measures of Center. Boxplots. Measures of Dispersion. Effect of Transformation of Variables (Optional). Samples and Populations: Statistical Inference. Perspective. 3. Random Sampling, Probability, and the Binomial Distribution. Probability and the Life Sciences. Random Sampling. Introduction to Probability. Probability Trees. Probability Rules (Optional). Density Curves. Random Variables. The Binomial Distribution. Fitting a Binomial Distribution to Data (Optional). 4. The Normal Distribution. Introduction. The Normal Curves. Areas Under a Normal Curve. Assessing Normality. The Continuity Correction (Optional). Perspective. 5. Sampling Distributions. Basic Ideas. Dichotomous Observations. Quantitative Observations. Illustration of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distribution (Optional). Perspective. 6. Confidence Intervals. Statistical Estimation. Standard Error of the Mean. Confidence Interval. Planning a Study to Estimate. Conditions for Validity of Estimation Methods. Confidence Interval for a Population Proportion. Perspective and Summary. 7. Comparison of Two Independent Samples. Introduction. Standard Error of (y1 - y2). Confidence Interval. Hypothesis Testing: The t-test. Further Discussion of the t-test. One-Tailed Tests. More on Interpretation of Statistical Significance. Planning for Adequate Power (Optional). Student's t: Conditions and Summary. More on Principles of Testing Hypotheses. The Wilcoxon-Mann-Whitney Test. Perspective. 8. Statistical Principles of Design. Introduction. Observational Studies. Experiments. Restricted Randomization: Blocking and Stratification. Levels of Replication. Sampling Concerns (Optional). Perspective. 9. Comparison of Paired Samples. Introduction. The Paired-Sample t-Test and Confidence Interval. The Paired Design. The Sign Test. The Wilcoxon Signed-Rank Test. Further Considerations in Paired Experiments. Perspective. 10. Analysis of Categorical Data. Inference for Proportions: The Chi-Squared Goodness-of-Fit Test. The Chi-Squared Test for a 2 X 2 Contingency Table. Independence and Association in a 2 X 2 Contingency Table. Fisher's Exact Test (Optional). The r x k Contingency Table. Applicability of Methods. Confidence Interval for a Difference Between Proportions. Paired Data and 2 X 2 Tables (Optional). Relative Risk and the Odds Ratio (Optional). Summary of Chi-Squared Tests. 11. Comparing the Means of Many Independent Samples. Introduction. The Basic Analysis of Variance. The Analysis of Variance Model (Optional). The Global F-Test. Applicability of Methods. Two-Way ANOVA (Optional). Linear Combinations of Means (Optional). Multiple Comparisons (Optional). Perspective. 12. Linear Regression and Correlation. Introduction. The Fitted Regression Line. Parametric Interpretation of Regression: The Linear Model. Statistical Inference Concerning B1. The Correlation Coefficient. Guidelines for Interpreting Regression and Correlation. Perspective. Summary of Formulas. 13. A Summary of Inference Methods. Introduction. Data Analysis Samples. Appendices. Chapter Notes. Statistical Tables. Answers to Selected Exercises. Index. Index of Examples.
Jeff Witmer is a PhD statistician and Professor at Oberlin College. He is a Fellow of the American Statistical Association, previous Chair of the Section on Statistical Education of ASA, previous editor of STATS magazine, and a member of the editorial board of the Consortium for the Advancement of Undergraduate Statistics Education. His work has been supported by grants from the National Science Foundation and the National Institutes of Health. Andrew Schaffner is a PhD statistician and Professor at California Polytechnic State University--San Luis Obispo. Additionally, he serves as the staff research statistician for both the Cal Poly Environmental Biotechnology Institute and the Morro Bay National Estuary Program. Organizations that support his research include the National Institutes of Health, United States Department of Agriculture, and the US Environmental Protection Agency.