Permutation in R (3:33) - Module 3 | Coursera

Permutation in R (3:33)

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Statistics for Genomic Data Science

Johns Hopkins University

4.2 (302 ratings)

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28K Students Enrolled

Course 6 of 6 in the Genomic Data Science Specialization

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An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Skills You'll Learn

Statistics, Data Analysis, R Programming, Biostatistics

Reviews

4.2 (302 ratings)

5 stars
54.30%
4 stars
27.48%
3 stars
10.59%
2 stars
2.64%
1 star
4.96%

CJ

Jul 15, 2019

It is really great that told me lots of basic statistical information that I didn't know.

TK

Mar 24, 2019

Very good course and useful understanding statistical aspects of data.

From the lesson

Module 3

This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.

Module 3 Overview (1:07)1:07

Logistic Regression (7:03)7:03

Regression for Counts (5:02)5:02

GLMs in R (9:28)9:28

Inference (4:18)4:18

Null and Alternative Hypotheses (4:45)4:45

Calculating Statistics (5:11)5:11

Comparing Models (7:08)7:08

Calculating Statistics in R9:46

Permutation (3:26)3:26

Permutation in R (3:33)3:33

P-values (6:04)6:01

Multiple Testing (8:25)8:25

P-values and Multiple Testing in R: Part A (5:58)5:58

P-values and Multiple Testing in R: Part B (4:23)4:23

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics

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