## About the Course

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