• Comparing representations of 2 categorical variables
• Calculating statistics for 2 categorical variables
• Representing bivariate data and interpreting correlation
• Linear regression models
• Residuals and residual plots
• Departures from linearity

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• Planning a study
• Sampling Methods
• Sources of bias in sampling methods
• Designing an experiment
• Interpreting the results of an experiment

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• Using simulation to estimate probabilities
• Calculating the probability of a random event
• Random variables and probability distributions
• The geometric dsitribution

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• Variation in statistics for samples collected from the same population
• The central limit theorem
• Biased and unbiased point estimates
• Sampling distributions for sample proportions
• Sampling distributions for sample means

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• Constructing and interpreting confidence intervals for a population proportion
• Setting up and carrying out a test for a population proportion
• Interpreting a p-value and justifying a claim about a population proportion
• Type I and Type II errors in significance testing
• Confidence intervals and tests for the difference of 2 proportions

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• Constructing and interpreting a confidence interval for a population mean
• Setting up and carrying out a test for a population mean
• Interpreting a p-value and justifying a claim about a population mean
• Confidence intervals and tests for the difference of 2 population means

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• The chi-square test for goodness of fit
• The chi-square test for homogeneity
• The chi-square test for independence
• Selecting an appropriate inference procedure for categorical data

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• Confidence intervals for the slope of a regression model
• Setting up and carrying out a test for the slope of a regression model
• Selecting an appropriate inference procedure

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