Introduction to Statistical Data Analysis for the Life Sciences: 2nd Edition (Paperback) book cover

Introduction to Statistical Data Analysis for the Life Sciences

2nd Edition

By Claus Thorn Ekstrom, Helle Sørensen

Chapman and Hall/CRC

526 pages | 101 B/W Illus.

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pub: 2014-11-06
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Description

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.

Table of Contents

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.

About the Originator

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED090000
MEDICAL / Biostatistics