Topics

• Slides, also in HTML format.
• Learning checks.
• ModernDive textbook. Feedback form here.

Exam Week Schedule

• Office Hours: Mon 5/15 1pm-4pm
• Office Hours: Tue 5/16 9am-12pm and 1pm-2pm. Only sporadic access to Slack until Midterm III.
• Midterm III: Fri 5/19 7pm-10pm in Warner 506
• Office Hours: Sun 5/21 1pm-5pm
• Office Hours: Mon 5/22 1pm-5pm
• Final Project Due: Tue 5/23 12pm. See Final Project Guidelines.
• Exit survey posted here.

4. Regression

• Lec41 - Mon 5/15: Midterm III Review + Course Response Evals
• Lec40 - Fri 5/12: Multiple Regression Part II
• Lec39 - Thu 5/11: Multiple Regression
• Lec38 - Wed 5/10: Categorical Predictors
• Lec37 - Mon 5/8: Least-Squares Line + Regression Output
• Lec36 - Thu 5/4: Correlation

3. Statistical Inference

c) Confidence Intervals

• Lec35 - Wed 5/3: Confidence Intervals in General
• Lec34 - Mon 5/1: Confidence Intervals
• Lec33 - Fri 4/28: Sampling Distributions and Standard Errors
• Lec32 - Thu 4/27: Back to Sampling
  • Handout: The Economist Article How did the polls get it wrong?
• Lec31 - Wed 4/26: Background Statistical Theory

b) Hypothesis Testing

• Lec30 - Mon 4/24: Finishing Hypothesis Testing
• Lec29 - Thu 4/20: Permutation Test
• Lec28 - Wed 4/19: Constructing the Null Hypothesis
  • Download grades.csv.
• Lec27 - Mon 4/17: Tying Hypothesis Testing with Sampling
• Lec26 - Fri 4/14: p-Values
• Lec25 - Thu 4/13: Hypothesis Testing Framework and Terminology
• Lec24 - Wed 4/12: Going over Midterm II
• Lec23 - Mon 4/10: Midterm II Review
  • Example Midterm II.
• Lec22 - Fri 4/7: Lady Tasting Tea

a) Probability Background

• Lec21 - Thu 4/6: Confounding Variables and Designed Experiments
• Lec20 - Wed 4/5: Introduction to Sampling
• Lec19 - Mon 4/3: Intro to Probability via Simulation
• Lec17 - Wed 3/22: Intro to Sampling Terminology

2. Data

e) Putting It All Together

• Final Project: Guidelines.
• Lec18 - Fri 3/24: The tao of data analysis.

d) Importing Data

• Lec16 - Mon 3/20: Importing Data
  • Install tidyverse package. Umbrella package of several useful packages.
  • Download DD_vs_SB.csv.

c) Manipulation AKA Wrangling

• Lec15 - Fri 3/17: 5MV#5 arrange() & _join
• Lec14 - Thu 3/16: 5MV#3 group_by() & 5MV#4 mutate()
• Lec13 - Wed 3/15: Piping %>%, 5MV#1 filter() & 5MV#2 summarize()
• Lec12 - Mon 3/13: Intro to Data Wrangling + Intro to R Markdown

b) Visualization

• Lec11 - Thu 3/9: 5NG#5 Barplots. We deconstruct boxplots i.e. geom_bar().
• Lec10 - Mon 3/6: Midterm I Review
  • Example Midterm I.
• Lec09 - Thu 3/2: 5NG#4 Boxplots. We deconstruct boxplots i.e. geom_boxplot().
• Lec08 - Wed 3/1: 5NG#3 Histograms + facets. We deconstruct histograms i.e. geom_histogram() + We introduce the technique of faceting.
• Lec07 - Mon 2/27: 5NG#2 Linegraphs. We deconstruct linegraphs i.e. geom_line().
• Lec06 - Fri 2/24: 5NG#1 Scatterplots. We deconstruct scatterplots i.e. geom_point().
• Lec05 - Thu 2/23: More 5NG. Instead of reverse engineering graphics using the grammar, we now forward engineer them.
• Lec04 - Wed 2/22: 5NG. Constructing statistical graphics in a ‘grammatical’ fashion and introducing the Five Named Graphs.

a) Representation

• Lec03 - Mon 2/20: Tidy Data. A common way of representing data

1. Introduction & Tools

• Lec02 - Thu 2/16: R Packages. Extending R’s base functionality with packages.
• Lec01 - Mon 2/13: Introduction. We discuss the syllabus, the pedogical thinking behind its design and introduce R, RStudio, and DataCamp.