# Fall 2016 Topic List

In reverse chronological order (a more detailed outline can be found here).

## 4. Regression

- Fri Dec 9 - Lec34 Finishing Regression: The coda to the course.
- Wed Dec 7 - Lec33 Residuals: The error, the lack of fit, what's leftover, the peanut butter at the bottom of the jar.
- Mon Dec 5 - Lec32 Linear Regression: Best fitting lines.

## 3. Statistical Inference

### c) Confidence Intervals

- Fri Dec 2 - Lec31 Finishing Confidence Intervals: Confidence intervals in practice.
- Thu Dec 1 - Lec30 Back to Confidence Intervals: Tying the concepts of standard errors and confidence intervals together.
- Wed Nov 30 - Lec29 Standard Errors Part II: More on standard errors.
- Mon Nov 28 - Lec28 Sampling Distributions and Standard Errors: Understanding how sampling variability affects estimates.
- Mon Nov 21 - Lec27-B Confidence Intervals: Catching a fish with a net and not a spear.

### b) Hypothesis Testing

- Mon Nov 21 - Lec27-A Hypothesis Testing Part V: There is only one test!
- Fri Nov 18 - Lec26 Hypothesis Testing Part IV: Casting hypothesis testing in terms of sampling.
- Wed Nov 16 - Lec25 Hypothesis Testing Part III: Permutations under (i.e. assuming) the null hypothesis.
- Mon Nov 14 - Midterm III Review + Lec24: Everything up to and including Lec24, but not p-values.
- Fri Nov 11 - Discussion: Whiteboard incident and election.
- Wed Nov 9 - Lec23 Hypothesis Testing Part II: Introducing the framework and terminology.
- Mon Nov 7 - Lec22 Hypothesis Testing: The example of the Lady Tasting Tea.

### a) Background

- Fri Nov 4 - Lec21 Randomized Experiments: Correlation vs causation and principles of designing experiments.
- Wed Nov 2 - Lec20 Different Types of Sampling: Sampling with and without replacement.
- Mon Oct 31 - Lec19 Intro to Probability via Simulation: Tapping into R's random number generating capabilities.
- Wed Oct 26 - Lec17 Intro to Statistical Inference: We lay out the sampling paradigm and introduce 7 important definitions related to statistical sampling.

## 2. Data

### d) Importing Data (Note Lec18 is here)

- Fri Oct 28 - Lec18 Importing Data into R: Via the Comma-Separated Values (CSV) spreasheet file format.

### c) Manipulation/Wrangling

- Mon Oct 24 - Midterm II Review: Everything up to and including Lec16.
- Fri Oct 21 - Lec16 Finishing the 5MV: arrange() variables in data frame and join() data frames.
- Wed Oct 19 - Lec15 More of the 5MV: Creating new variables by mutate() existing ones.
- Fri Oct 14 - Lec14 More Data Manipulation: %>% piping and summarise() observations that have been group_by() another variable.
- Wed Oct 12 - Lec13 Intro to Data Manipulation: We introduce the Five Main Verbs (5MV) for data manipulation. Today: select() columns i.e. variables and filter() rows.

### b) Visualization

- Mon Oct 10 - Lec12 Barplots: We deconstruct barplots i.e. geom_barplot().
- Fri Oct 7 - Lec11 Histograms: We deconstruct histograms i.e. geom_histogram().
- Mon Oct 3 - Lec10 Boxplots Part 2: Finishing boxplots. Also Midterm I review.
- Fri Sep 30 - Lec09 Boxplots: We deconstruct boxplots i.e. geom_boxplot().
- Wed Sep 28 - Lec08 Linegraphs: We deconstruct linegraphs i.e. geom_line().
- Mon Sep 26 - Lec07 Scatterplots: We deconstruct scatterplots i.e. geom_point().
- Fri Sep 23 - Lec06 More Grammar of Graphics: Last time we reversed engineered visualizations, today we (forward) engineer them.
- Wed Sep 21 - Lec05 Grammar of Graphics: Constructing statistical graphics in a 'grammatical' fashion and introducing the Five Named Graphs (5NG).

### a) Representation

- Mon Sep 19 - Lec04 Tidy Data: Rows of observations, columns of variables, and tables of matching observational units.

## 1. Introduction & Tools

- Fri Sep 16 - Lec03 R Packages and Intro to R Markdown: Learning about R packages and introducing R Markdown for reproducible research.
- Wed Sep 14 - Lec02 Intro to R: Getting familiar with the command line and learning the basics of R.
- Mon Sep 12 - Lec01 Getting Started: We discuss the syllabus and the pedogical thinking behind its design and introduce R and RStudio.