# Introduction to Statistical Data Analysis for the Life Sciences

Publisher:
Chapman & Hall/CRC
Number of Pages:
415
Price:
69.95
ISBN:
9781439825556
Tuesday, September 28, 2010
Reviewable:
No
Include In BLL Rating:
No
Claus Thorn Ekstrøm and Helle Sørensen
Publication Date:
2011
Format:
Paperback
Audience:
Category:
Textbook

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
Perspective

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

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

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

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

Model Validation and Prediction
Model validation
Prediction

Linear Normal Models
Multiple linear regression
Linear models
Interactions between variables

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

Analysis of Count Data
The chi-square test for goodness-of-fit
2 × 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

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

Appendix A: Summary of Inference Methods
Statistical concepts
Statistical analysis
Model selection

Appendix B: Introduction to R
Working with R
Data frames and reading data into R
Manipulating data
Graphics with R
Reproducible research
Installing R
Exercises

Appendix C: Statistical Tables
The x2 distribution
The normal distribution
The t distribution
The F distribution

Bibliography

Index

Publish Book:
Modify Date:
Wednesday, January 12, 2011