## Basic Information

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

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.

## MATLAB Lectures

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)

## Statistics Lectures

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

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