- Robust regression
- Logistic regression
- Exact logistic regression
- Multinomial logistic regression
- Ordinal logistic regression
- Probit regression
- Poisson regression
- Negative binomial regression
- Zero-inflated Poisson regression
- Zero-inflated negative binomial regression
- Zero-truncated Poisson
- Zero-truncated negative binomial
- Censored and truncated regression
- To bit regression
- Truncated regression
- Interval regression
- Multivariate analysis
- Canonical correlation analysis
- Mixed effect models
- Mixed effects logistic regression models
- Graphical displays: stem plots, histograms, box plots, scatter plots
- Numerical summaries: mean, median, quartiles, variance, standard deviation
- Normal distributions: assessing normality, normal probability plots
- Categorical data: two-way tables, bar graphs, segmented bar graphs
- Linear regression and correlation
- Linear regression: least-squares, residuals, outliers and influential observations, extrapolation
- Correlation: correlation coefficient, r²
- Inference in linear regression: confidence intervals for intercept and slope, significance tests, mean response and prediction intervals.
- Multiple linear regression: confidence intervals, tests of significance, squared multiple correlation
- ANOVA for regression: analysis of variance calculations for simple and multiple regression,
f statistics
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- Experiments and sampling
- Experimental design: experimentation, control, randomization, replication
- Sampling: simple, stratified, and multistage random sampling
- Sampling in statistical inference: sampling distributions, bias, variability
- Probability
- Probability models: components of probability models, basic rules of probability
- Conditional probability: probabilities of intersections of events, bayes’s formula
- Random variables: discrete, continuous, density functions
- Mean and variance of random variables: definitions, properties
- Binomial distributions: counts, proportions, normal approximation
- Sample means: mean, variance, distribution, central limit theorem
- Hypothesis tests and confidence intervals
- Confidence intervals: inference about population mean, z and t critical values
- Tests of significance: null and alternative hypotheses for population mean, one-sided and two-sided z and t tests, levels of significance, matched pair analysis
- Comparison of two means: confidence intervals and significance tests, z and t statistics, pooled t procedures
- Inference for categorical data: confidence intervals and significance tests for a single proportion, comparison of two proportions
- Chi-square goodness of fit test: chi-square test statistics, tests for discrete and continuous distributions
- Two-way tables and the chi-square test: categorical data analysis for two variables, tests of association
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