Introduction to Statistical Data Analysis for the Life Sciences, Second Edition (2nd Revised edition)
By: Claus Thorn Ekstrom (author), Helle Sorensen (author)Paperback
A Hands-On Approach to Teaching Introductory Statistics Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets. New to the Second Edition * A new chapter on non-linear regression models * A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken * Additional exercises in most chapters * A summary of statistical formulas related to the specific designs used to teach the statistical concepts This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R.
For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.
Description of Samples and Populations Data types Visualizing categorical data Visualizing quantitative data Statistical summaries What is a probability? R Linear Regression Fitting a regression line When is linear regression appropriate? The correlation coefficient Perspective R Comparison of Groups Graphical and simple numerical comparison Between-group variation and within-group variation Populations, samples, and expected values Least squares estimation and residuals Paired and unpaired samples Perspective R The Normal Distribution Properties One sample Are the data (approximately) normally distributed? The central limit theorem R Statistical Models, Estimation, and Confidence Intervals Statistical models Estimation Confidence intervals Unpaired samples with different standard deviations R Hypothesis Tests Null hypotheses t-tests Tests in a one-way ANOVA Hypothesis tests as comparison of nested models Type I and type II errors R Model Validation and Prediction Model validation Prediction R Linear Normal Models Multiple linear regression Additive two-way analysis of variance Linear models Interactions between variables R Non-Linear Regression Non-linear regression models Estimation, confidence intervals, and hypothesis tests Model validation R Probabilities Outcomes, events, and probabilities Conditional probabilities Independence The Binomial Distribution The independent trials model The binomial distribution Estimation, confidence intervals, and hypothesis tests Differences between proportions R Analysis of Count Data The chi-square test for goodness-of-fit 2 x 2 contingency table Two-sided contingency tables R Logistic Regression Odds and odds ratios Logistic regression models Estimation and confidence intervals Hypothesis tests Model validation and prediction R Statistical Analysis Examples Water temperature and frequency of electric signals from electric eels Association between listeria growth and RIP2 protein Degradation of dioxin Effect of an inhibitor on the chemical reaction rate Birthday bulge on the Danish soccer team Animal welfare Monitoring herbicide efficacy Case Exercises Case 1: Linear modeling Case 2: Data transformations Case 3: Two sample comparisons Case 4: Linear regression with and without intercept Case 5: Analysis of variance and test for linear trend Case 6: Regression modeling and transformations Case 7: Linear models Case 8: Binary variables Case 9: Agreement Case 10: Logistic regression Case 11: Non-linear regression Case 12: Power and sample size calculations Appendix A: Summary of Inference Methods Appendix B: Introduction to R Appendix C: Statistical Tables Appendix D: List of Examples Used throughout the Book Bibliography Index Exercises appear at the end of each chapter.
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- ID: 9781482238938
2nd Revised edition
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