# Detailed class plan

For this class, the largest part of your fact consumption will be through reading the text book. You can expect a workload of approximately 30 pages each week (mean: 29; std.dev.: 6.7; 5-number summary: 20 / 24.25 / 27.5 / 35 / 40), as well as approximately 10 assigned exercises.

The overall structure of our class meetings will be:

- Mondays:

- I clarify any issues that showed up in your self reflections.
- We discuss the points raised for thinking through and discussing in the text.
- I introduce additional important or helpful material and perspectives.
- Peer-evaluation of homework exercises.

- Wednesdays, classroom:

- I go through the concepts and computer programming issues relevant for the lab of the day.

- Wednesdays, computer lab:

- You work through a lab sheet, introducing and teaching specific techniques in RStudio.

You may optionally work in Python/PyLab. I am happy to help – this is the environment I work the most in – but I will not be able to talk about the Python approach in the Wednesday lecture part.

# Lab content

- Get to know RStudio. Using
`ggplot2`

for graphs. Histograms, scatterplots, jitter, alpha-channel. Reproducibility & random seeding. Dataset loading. - Compute summary statistics. Explore failures of Mean/Variance measures. Plotting PDFs. Computing PDFs, CDFs, correlation coefficients. Dataset summaries.
*Experiment design, sampling design*Data provenance.`N/A`

representation.`N/A`

handling.`-999`

as null value.`Mrs. Null`

.*Inference, Ethics, Causation*Anscombe quartet.*Randomness, models, random variables**Least squares*Computing and examining LSQ fits and summaries- Error types: base rate bias.
*Sampling distributions*Polling data?*Confidence intervals, significance*Data fishing, torture a data set. Polling data.*Proportional inference*Exit polls/results.*Means inference**Two-way tables; chi-squared**Linear regression**ANOVA*