The goal of this course is to (1) identify and explain basic statistical principles that are widely applicable to the analysis of neuroscience and behavioral data and (2) show how these principles can be translated into practice using MATLAB as the programming environment. Topics will include probability distributions, error bars and confidence intervals, statistical significance, regression, classification, correlation, linear and nonlinear models, cross-validation, bootstrapping, model selection, and randomization methods. We will focus on nonparametric and computational approaches to statistical problems, as opposed to classical statistical approaches involving parametric assumptions and analytic solutions. This course is intended for graduate students or postdocs who would like to gain a better understanding of statistical principles and/or learn how to program in MATLAB. Auditors are welcome.

Instructor:Kendrick Kay, PhD (e-mail | web site)

Syllabus:Syllabus

**1. MATLAB Basics I**

Lecture notes | MATLAB video (58 min) | MATLAB transcript

** 2. MATLAB Basics II**

Lecture notes | MATLAB video (1h 47 min) | MATLAB transcript

MATLAB examples (pdf | html | source code)

**1. Probability distributions and error bars**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (29 min)

**2. Hypothesis testing and correlation**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (68 min)

**3. MATLAB examples**

Examples (pdf | html | source code)

Demonstration of examples (MATLAB video (42 min) | MATLAB transcript)

In-class exercises (pdf)

**4. Model specification**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (51 min)

**5. Model fitting**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (67 min)

**6. MATLAB examples**

Examples (pdf | html | source code)

Demonstration of examples (MATLAB video (28 min) | MATLAB transcript)

In-class exercises (pdf | extra file: outputfcnplot.m)

**7. Model accuracy**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (57 min)

**8. Model reliability**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (42 min)

**9. MATLAB examples**

Examples (pdf | html | source code)

Demonstration of examples (MATLAB video (20 min) | MATLAB transcript)

In-class exercises (pdf)

**10. Classification**

Lecture notes | Lecture slides | Lecture slides (PPT) | Lecture slides (animated) | Lecture video (77 min)

**11. MATLAB examples**

Examples (pdf | html | source code)

Demonstration of examples (MATLAB video (30 min) | MATLAB transcript)

In-class exercises (pdf)

**Homework 1: MATLAB Basics**

Homework (+ data file) | Solutions

**Homework 2: Statistics Lectures 1–2**

Homework (+ data file) | Solutions

**Homework 3: Statistics Lectures 3–4**

Homework (+ data file) | Solutions

**Homework 4: Statistics Lectures 5–6**

Homework (+ data file) | Solutions

**Homework 5: Statistics Lecture 7**

Homework (+ data file) | Solutions