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Introduction to statistical data analysis for the life sciences / Claus Thorn Ekstrøm, Helle Sørensen. -- 2nd ed. -- Boca Raton : CRC Press, c2015. – (58.1057/E36s/2nd ed.) |
Contents
Contents
Preface
1 Description of samples and populations
1.1 Data types
1.2 Visualizing categorical data
1.3 Visualizing quantitative data
1.4 Statistical summaries
1.5 What is a probability?
1.6 R
1.7 Exercises
2 Linear regression
2.1 Fitting a regression line
2.2 When is linear regression appropriate?
2.3 The correlation coefficient
2.4 Perspective
2.5 R
2.6 Exercises
3 Comparison of groups
3.1 Graphical and simple numerical comparison
3.2 Between-group variation and within-group variation
3.3 Populations， samples， and expected values
3.4 Least squares estimation and residuals
3.5 Paired and unpaired samples
3.6 Perspective
3.7 R
3.8 Exercises
4 The normal distribution
4.1 Properties
4.2 One sample
4.3 Are the data (approximately) normally distributed?
4.4 The central limit theorem
4.5 R
4.6 Exercises
5 Statistical models， estimation， and confidence intervals
5.1 Statistical models
5.2 Estimation
5.3 Confidence intervals
5.4 Unpaired samples with different standard deviations
5.5 R
5.6 Exercises
6 Hypothesis tests
6.1 Null hypotheses
6.2 t-tests
6.3 Tests in a one-way ANOVA
6.4 Hypothesis tests as comparison of nested models
6.5 Type I and type II errors
6.6 R
6.7 Exercises
7 Model validation and prediction
7.1 Model validation
7.2 Prediction
7.3 R
7.4 Exercises
8 Linear normal models
8.1 Multiple linear regression
8.2 Additive two-way analysis of variance
8.3 Linear models
8.4 Interactions between variables
8.5 R
8.6 Exercises
9 Non-linear regression
9.1 Non-linear regression models
9.2 Estimation， confidence intervals， and hypothesis tests
9.3 Model validation
9.4 R
9.5 Exercises
10 Probabilities
10.1 Outcomes， events， and probabilities
10.2 Conditional probabilities
10.3 Independence
10.4 Exercises
11 The binomial distribution
11.1 The independent trials model
11.2 The binomial distribution
11.3 Estimation， confidence intervals， and hypothesis tests
11.4 Differences between proportions
11.5 R
11.6 Exercises
12 Analysis of count data
12.1 The chi-square test for goodness-of-fit
12.2 2 x 2 contingency table
12.3 Two-sided contingency tables
12.4 R
12.5 Exercises
13 Logistic regression
13.1 Odds and odds ratios
13.2 Logistic regression models
13.3 Estimation and confidence intervals
13.4 Hypothesis tests
13.5 Model validation and prediction
13.6 R
13.7 Exercises
14 Statistical analysis examples
14.1 Water temperature and frequency of electric signals from electric eels
14.2 Association between listeria growth and RIP2 protein
14.3 Degradation of dioxin
14.4 Effect of an inhibitor on the chemical reaction rate
14.5 Birthday bulge on the Danish soccer team
14.6 Animal welfare
14.7 Monitoring herbicide efficacy
15 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 452
A Summary of inference methods
A.1 Statistical concepts
A.2 Statistical analysis
A.3 Model selection
A.4 Statistical formulas
B Introduction to R
B.1 Working with R
B.2 Data frames and reading data into R
B.3 Manipulating data
B.4 Graphics with R
B.5 Reproducible research
B.6 Installing R
B.7 Exercises
C Statistical tables 493
C.1 The X2 distribution 493
C.2 The normal distribution 494
C.3 The t distribution 496
C.4 The F distribution
D List of examples used throughout the book
Bibliography
Index
