V: Generalized linear and nonlinear models
35
Logistic regression
Statistical Methods
Preface
I: Sets, probability, and random variables
1
Basics of sets
2
Basics of probability
3
Counting rules
4
Conditional probability
5
Independence
6
Bayes’ rule
7
Random variables
8
Discrete random variables
9
Bernoulli and binomial distributions
10
Hypergeometric distribution
11
Drawing with versus without replacement
12
Continuous random variables
13
The Normal distribution
14
Poisson and exponential distributions
II: Random samples
15
The random sample
16
Mean of a random sample
17
Histogram
18
The sample variance
19
Normal quantile-quantile plot
20
The central limit theorem
21
Sample proportion
22
Normal approximation to the binomial
III: Inference from random samples
23
Statistical inference
24
First confidence interval for a normal mean
25
Confidence interval for a normal variance
26
Revised confidence interval for a normal mean
27
Large-sample confidence interval for a mean
28
Confidence interval for a proportion
29
Sample size determination
30
Randomized experiments versus observational studies
31
One-way ANOVA
32
Statistical power of one-sample tests
IV: Linear models
33
Simple linear regression
34
Multiple linear regression I
V: Generalized linear and nonlinear models
35
Logistic regression
Appendices
36
\(z\)
table
37
Chi-squared table
38
\(t\)
table
References
V: Generalized linear and nonlinear models
35
Logistic regression
35
Logistic regression
$$
$$
34
Multiple linear regression I
36
\(z\)
table