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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
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
Exercises appear at the end of each chapter.