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Introduction to statistical data analysis for the life sciences / Claus Thorn Ekstrom and Helle Sorensen. — Boca Raton : CRC Press, c2011. – (58.1057/E36s) |
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 Probabilities
9.1 Outcomes， events， and probabilities
9.2 Conditional probabilities
9.3 Independence
9.4 Exercises
10 The binomial distribution
10.1 The independent trials model
10.2 The binomial distribution
10.3 Estimation， confidence intervals， and hypothesis tests
10.4 Differences between proportions
10.5 R
10.6 Exercises
11 Analysis of count data
11.1 The chi-square test for goodness-of-fit
11.2 2 x 2 contingency table
11.3 Two-sided contingency tables
11.4 R
11.5 Exercises
12 Logistic regression
12.1 Odds and odds ratios
12.2 Logistic regression models
12.3 Estimation and confidence intervals
12.4 Hypothesis tests
12.5 Model validation and prediction
12.6 R
12.7 Exercises
13 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
A Summary of inference methods
A.1 Statistical concepts
A.2 Statistical analysis
A.3 Model selection
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
C.1 The X2 distribution
C.2 The normal distribution
C.3 The t distribution
C.4 The F distribution
Bibliography
Index
