Chapter 2 Class Schedule
As mentioned, the course divides neatly into three parts.
- Part A begins in Class 2 and is about Visualizing Data.
- Part B begins in Class 10 or so, and is about Making Comparisons.
- Part C begins with class 19 or so. It’s about Building Regression Models.
But within those Parts, we jump around. A lot.
The first five classes are more about R and R Studio than about statistics. Once everyone’s gotten rolling, our approach changes a bit to focus more on statistical concerns, and less on the technology.
The Topics listed here will not correspond closely to this schedule, but we’ll wind up in the same place.
| Class | Date | Topics | Readings |
|---|---|---|---|
| 1 | 2017-08-29 | Introduction, Getting Started | Syllabus |
| 2 | 2017-08-31 | Part A. Visualizing Data, R and R Studio | |
| 3 | 2017-09-05 | Exploring Data and the Tidyverse | |
| 4 | 2017-09-07 | (Room E401) Scripts and Projects in R/R Studio | Leek 5, 9, 10, 13 |
| 5 | 2017-09-12 | Data Transformation and dplyr | Silver, Intro and Chapter 1 |
| 6 | 2017-09-14 | Exploratory Data Analysis | |
| - | 2017-09-15 | Assignment 1 due at noon. | |
| 7 | 2017-09-19 | Descriptive Statistics, The Normal Distribution | |
| 8 | 2017-09-21 | Using ggplot2 more effectively |
|
| - | 2017-09-22 | Assignment 2 due at noon. | |
| 9 | 2017-09-26 | Linear regression and correlation | Silver, 2-3 |
| 10 | 2017-09-28 | Studying associations | |
| - | 2017-09-29 | Assignment 3 due at noon. | |
| 11 | 2017-10-03 | Part B: Making Comparisons | Leek 1-4 and 12 |
| 12 | 2017-10-05 | Confidence intervals for Means | Quiz 1 provided to you. |
| - | 2017-10-09 | Quiz 1 due at noon. | |
| 13 | 2017-10-10 | Matched Pairs, Independent Samples | Leek 6, Silver 4-5 |
| 14 | 2017-10-12 | Hypothesis Testing Strategies | |
| - | 2017-10-13 | Project Task A due at noon. | |
| 15 | 2017-10-17 | The Analysis of Variance and Related Tools | Silver 7-8 |
| 16 | 2017-10-19 | Multiple Comparisons | |
| - | 2017-10-23 | Project Task B due at noon. | |
| – | 2017-10-24 | CWRU Fall Break (no class) | – |
| 17 | 2017-10-26 | Comparing Two Means - Set Up Survey | |
| - | 2017-10-27 | Assignment 4 due at noon. | |
| 18 | 2017-10-31 | ANOVA - Comparing Multiple Means | |
| 19 | 2017-11-02 | Part C: Building Effective Models | Silver through 11 |
| 20 | 2017-11-07 | Building Inferences for Rates | |
| - | 2017-11-08 | Project Task C due at noon. | |
| - | 2017-11-09 | Assignment 5 due at noon. | |
| 21 | 2017-11-09 | Conclude Part B of course | Quiz 2 provided to you. |
| - | 2017-11-14 | Quiz 2 due at 8 AM. | |
| 22 | 2017-11-14 | Begin Part C of course | Leek 7-8 |
| 23 | 2017-11-16 | Estimation and Inference | |
| - | 2017-11-20 | Project Task D due at noon. | |
| – | 2017-11-21 | Class is cancelled. | – |
| – | 2017-11-23 | CWRU Thanksgiving Break (no class) | – |
| 24 | 2017-11-28 | Residual and Influence Analysis | Silver 12 |
| 25 | 2017-11-30 | Prediction and Validation | |
| - | 2017-12-04 | Assignment 6 due at noon. | |
| 26 | 2017-12-05 | More Modeling Content | Silver finish book |
| 27 | 2017-12-07 | Looking Back, and Forward | Quiz 3 provided to you. |
| - | 2017-12-12 | Quiz 3 due at noon. | – |
| - | 2017-12-13 | Project Task E due at noon. |
Final Portfolio Presentations will be held on
- Monday 2017-12-11
- Tuesday 2017-12-12, and
- Thursday 2017-12-14
Project presentations will be scheduled as part of Project Task A, and you will know the date and time of your portfolio presentation by 2017-10-19.
2.1 Topics Discussed Extensively in 431 and its follow-up, 432
- Exploratory Data Analysis: “All graphs are comparisons” including data exploration, statistical graphics and more general visualization of information.
- Placing biological, medical and health research questions into a statistical framework.
- Study Development - making choices in designing and executing the collection and aggregation of data.
- Data Handling - including important issues in importing, tidying and transforming data, as well as methods for dealing with missing data, including imputation.
- Statistical Comparisons: “All of statistics are comparisons” - including methods for discrete and continuous variables: intervals, assumptions, some thoughts on statistical power, and the bootstrap, design of visualizations and models for rates, proportions and contingency tables.
- The proper use of multi-predictor models for continuous and discrete data, including…
- Fitting, evaluating, and interpreting linear and generalized linear models.
- Prediction and validation.
- Critical role of graphics, including diagnostics and residual analysis.
- Model choice, including variable selection, shrinkage and model uncertainty.
- Dealing with categorical predictors and interactions meaningfully.
- Causal inference using regression: controlling for covariates meaningfully.
- Using R and R Studio to make all of the things above happen; with particular emphasis on doing replicable research and using Markdown to document the work.
Need more details on course topics? Check out Dr. Love’s Notes.