Course info

In our training to become statistics practitioners, we will do the following:

1. Practice designing experiments that are synergized with appropriate statistical analyses.

2. Practice interpreting the results of statistical analyses.

3. Develop enough fundamental statistics knowledge to do #1 and #2.

4. Practice running statistical analyses in R to do basic stuff and have the skills to figure out more complicated stuff.

Here is an outline of specific topics we will cover:

Review

• Running basic tests in R

  • Confidence intervals

  • Chi-square

  • T-test

  • One-way ANOVA

  • Assessing type 1 and 2 error

• Hand calculation of important values

  • Residuals

  • Sum of squares

  • F-statistics

Planning a study

• Determining sample sizes (power analysis)

• Randomization techniques

• Within vs. between subject designs

• Counterbalancing

• How to sample (some theory)

Data wrangling

• R data handling

• Identifying outliers

• Exploratory plots

• Assumptions

Analyzing data

• Just getting by

• Planning ahead

• Confidence intervals, p-values, effect sizes

• (Everything is) REGRESSION -> single & multiple

• Dependent ANOVA (GLM, GAM)

• Model assessment (BIC, AIC)

• Bayesian statistics