A. Exploratory Data Analysis

1. Multi-vari studies
   Use multi-vari studies to interpret the difference between positional, cyclical and temporal variation; design sampling plans to investigate the largest sources of variation; create and interpret multi-vari charts. (Application)
2. Measuring and modeling relationships between variables
   a. Simple and multiple least squares linear regression
   Calculate the regression equation; apply and interpret hypothesis tests for regression statistics; use the regression model for estimation and prediction, and analyze the uncertainty in the estimate. (Models that have nonlinear parameters will not be tested). (Evaluation)
   b. Simple linear correlation
   Calculate and interpret the correlation coefficient and its confidence interval; apply and interpret a hypothesis test for the correlation coefficient; understand the difference between correlation and causation. (Serial correlation will not be tested). (Evaluation)
   c. Diagnostics
   Analyze residuals of the model. (Analysis)

B. Hypothesis Testing

1. Fundamental concepts of hypothesis testing
   a. Statistical vs. practical significance
   Define, compare and contrast statistical and practical significance. (Evaluation)
   b. Significance level, power, type I and type II errors
   Apply and interpret the significance level, power, type I and type II errors of statistical tests. (Evaluation)
   c. Sample size
   Understand how to calculate sample size for any given hypothesis test. (Application)
2. Point and interval estimation
   Define and interpret the efficiency and bias of estimators; compute, interpret and draw conclusions from statistics such as standard error, tolerance intervals and confidence intervals; understand the distinction between confidence intervals and prediction intervals. (Analysis)
3. Test for means, variances and proportions
   Apply hypothesis tests for means, variances and proportions, and interpret the results. (Evaluation)
4. Paired-comparison tests
   Define, determine applicability and apply paired-comparison parametric hypothesis tests, and interpret the results. (Evaluation)
5. Goodness-of-fit tests
   Define, determine applicability and apply chi-square tests and interpret the results. (Evaluation)
6. Analysis of Variance (ANOVA)
   Define, determine applicability and apply ANOVAs and interpret the results. (Evaluation)
7. Contingency tables
   Define, determine applicability, and construct a contingency table and use it to determine statistical significance. (Evaluation)
8. Nonparametric Tests
   Define, determined applicability and construct various nonparametric tests including Mood's Median, Levene's test, Kruskal-Wallis, Mann Whitney, etc. (Analysis)