 Robust regression
 Logistic regression
 Exact logistic regression
 Multinomial logistic regression
 Ordinal logistic regression
 Probit regression
 Poisson regression
 Negative binomial regression
 Zeroinflated Poisson regression
 Zeroinflated negative binomial regression
 Zerotruncated Poisson
 Zerotruncated 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: twoway tables, bar graphs, segmented bar graphs
 Linear regression and correlation
 Linear regression: leastsquares, 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


 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, onesided and twosided 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
 Chisquare goodness of fit test: chisquare test statistics, tests for discrete and continuous distributions
 Twoway tables and the chisquare test: categorical data analysis for two variables, tests of association
