Part I: General Concept and Techniques
Chapter 1: Measurement
1.1 What is biostatistics?
1.2 Organization of data
1.3 Types of measurements
1.4 Data quality
Chapter 2: Types of studies
2.1 Surveys
2.2 Comparative studies
Chapter 3: Frequency distributions
3.1 Stemplots
3.2 Frequency tables
3.3 Additional frequency charts
Chapter 4: Summary statistics
4.1 Central location: mean
4.2 Central location: median
4.3 Central location: mode
4.4 Comparison of the mean, median, and mode
4.5 Spread: quartiles
4.6 Boxplots
4.7 Spread: the variance and standard deviation
4.8 Selecting summary statistics
Chapter 5: Probability concepts
5.1 What is probability?
5.2 Types of random variables
5.3 Discrete random variables
5.4 Continuous random variables
5.5 More rules and properties of probability
Chapter 6: Binomial probability distributions
6.1 Binomial random variables
6.2 Calculating binomial probabilities
6.3 Cumulative probabilities
6.4 Probability calculators
6.5 Expected value and variance of a binomial random variable
6.6 Using the binomial pmf to help make judgments
Chapter 7: Normal probability distributions
7.1 Characteristics of Normal distributions
7.2 Determining Normal probabilities
7.3 Finding values that correspond to Normal probabilities
7.4 Assessing departures from Normality
Chapter 8: Introduction to statistical inference
8.1 Concepts
8.2 Sampling behavior of a mean
8.3 Sampling behavior of a proportion
Chapter 9: Basis of hypothesis testing
9.1 The null and alternative hypotheses
9.2 Test statistic
9.3 P-value
9.4 Statistical significance
9.5 One-sample z test (summary)
9.6 Power and sample size
Chapter 10: Basis of confidence intervals
10.1 Introduction to confidence intervals
10.2 Confidence interval for μ, σ known
10.3 Sample size requirements
10.4 Relationship between hypothesis testing and confidence intervals
Part II: Quantitative response variable
Chapter 11: Inference about a mean
11.1 Estimated standard error of the mean
11.2 Student’s t distributions
11.3 One-sample t test
11.4 Confidence interval for μ
11.5 Paired samples
11.6 Conditions for inference
11.7 Sample size and power
Chapter 12: Comparing independent means
12.1 Paired and independent samples
12.2 Exploratory and descriptive statistics
12.3 Inference about the mean difference
12.4 Equal variance t procedure (optional)
12.5 Conditions for inference
12.6 Sample size and power
Chapter 13: Comparing several means (one-way ANOVA)
13.1 Descriptive statistics
13.2 The Problem of Multiple Comparisons
13.3 Analysis of variance (ANOVA)
13.4 Post hoc comparisons
13.5 The equal variance assumption
13.6 Introduction to non-parametric tests
Chapter 14: Correlation and Regression
14.1 Data
14.2 Scatterplots
14.3 Correlation
14.4Regression
Chapter 15: Multiple Linear Regression
15.1 The general idea
15.2 The multiple linear regression model
15.3 Categorical explanatory variables in regression models
15.4 Regression coefficients
15.5 ANOVA for multiple linear regression
15.6 Examining multiple regression conditions
Part III: Categorical response variable
Chapter 16: Inference about a proportion
16.1 Proportions
16.2 The sampling distribution of a proportion
16.3 Hypothesis test, Normal approximation
16.4 Hypothesis test, binomial method
16.5 Confidence interval for population proportion p
16.6 Sample size and power
Chapter 17: Comparing two proportions
17.1 Data
17.2 Proportion difference (risk difference)
17.3 Hypothesis test
17.4 Proportion ratio (relative risk)
17.5 Systematic sources of error
17.6 Power and sample size
Chapter 18: Cross-tabulated counts
18.1 Types of samples
18.2 Describing naturalistic and cohort samples
18.3 Chi-square test of association
18.4 Test for trend
18.4 Case-control samples
18.5 Matched-pairs
Chapter 19: Stratified 2-by-2 Tables
19.1 Preventing confounding
19.2 Simpson’s paradox
19.3 Mantel-Haenszel methods
19.4 Interaction
Appendices
A. Table of 2000 Random Digits
B. Cumulative probabilities for a Standard Normal random variable (z Table)
C. t Table
D. F Table
E. χ2 Table
F. Two-tails of z