# Good Intro MOOC for Statistics in R

#1

DukeStats

The goals of this course are as follows:

• Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
• Use statistical software ® to summarize data numerically and visually, and to perform data analysis.
• Have a conceptual understanding of the unified nature of statistical inference. Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions.
• Model and investigate relationships between two or more variables within a regression framework. Interpret results correctly, effectively, and in context without relying on statistical jargon. Critique data-based claims and evaluate data-based decisions.
• Complete a research project that employs simple statistical inference and modeling techniques.

## Course Syllabus

Week 1: Unit 1 - Introduction to data
Part 1 – Designing studies
Part 2 – Exploratory data analysis
Part 3 – Introduction to inference via simulation

Week 2: Unit 2 - Probability and distributions
Part 1 – Defining probability
Part 2 – Conditional probability
Part 3 – Normal distribution
Part 4 – Binomial distribution

Week 3: Unit 3 - Foundations for inference
Part 1 – Variability in estimates and the Central Limit Theorem
Part 2 – Confidence intervals
Part 3 – Hypothesis tests

Week 4: Finish up Unit 3 + Midterm
Part 4 – Inference for other estimators
Part 5 - Decision errors, significance, and confidence

Week 5: Unit 4 - Inference for numerical variables
Part 1 – Comparing two means
Part 2 – Bootstrapping
Part 3 – Inference with the t-distribution
Part 4 – Comparing three or more means (ANOVA)

Week 6: Unit 5 - Inference for categorical variables
Part 1 – Single proportion
Part 2 – Comparing two proportions
Part 3 – Inference for proportions via simulation
Part 4 – Comparing three or more proportions (Chi-square)

Week 7: Unit 6 - Introduction to linear regression
Part 1 – Relationship between two numerical variables
Part 2 – Linear regression with a single predictor
Part 3 – Outliers in linear regression
Part 4 – Inference for linear regression

Week 8: Unit 7 - Multiple linear regression
Part 1 – Regression with multiple predictors
Part 2 – Inference for multiple linear regression
Part 3 – Model selection
Part 4 – Model diagnostics

Week 9: Review / catch-up week
Bayesian vs. frequentist inference

#2

I signed up for the last run, but couldn’t participate properly. This time. however, I’m a little more prepared and planning to spend more time with it. I’ve already got a head start by reading the Open Intro book.