2nd Edition

Introduction to Statistical Data Analysis for the Life Sciences

    526 Pages 101 B/W Illustrations
    by Chapman & Hall

    526 Pages
    by Chapman & Hall

    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?

    Linear Regression
    Fitting a regression line
    When is linear regression appropriate?
    The correlation coefficient

    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

    The Normal Distribution
    One sample
    Are the data (approximately) normally distributed?
    The central limit theorem

    Statistical Models, Estimation, and Confidence Intervals
    Statistical models
    Confidence intervals
    Unpaired samples with different standard deviations

    Hypothesis Tests
    Null hypotheses
    Tests in a one-way ANOVA
    Hypothesis tests as comparison of nested models
    Type I and type II errors

    Model Validation and Prediction
    Model validation

    Linear Normal Models
    Multiple linear regression
    Additive two-way analysis of variance
    Linear models
    Interactions between variables

    Non-Linear Regression
    Non-linear regression models
    Estimation, confidence intervals, and hypothesis tests
    Model validation

    Outcomes, events, and probabilities
    Conditional probabilities

    The Binomial Distribution
    The independent trials model
    The binomial distribution
    Estimation, confidence intervals, and hypothesis tests
    Differences between proportions

    Analysis of Count Data
    The chi-square test for goodness-of-fit
    2 x 2 contingency table
    Two-sided contingency tables

    Logistic Regression
    Odds and odds ratios
    Logistic regression models
    Estimation and confidence intervals
    Hypothesis tests
    Model validation and prediction

    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



    Exercises appear at the end of each chapter.


    Claus Thorn Ekstrom, Helle Sørensen