Regression for Counts (5:02) - Module 3 | Coursera

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Statistics for Genomic Data Science

Johns Hopkins University

4.2 (283 ratings) | 26K Students Enrolled

Course 7 of 8 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 (283 ratings)

5 stars
54.06%
4 stars
26.85%
3 stars
11.30%
2 stars
2.82%
1 star
4.94%

TK

Mar 24, 2019

Very good course and useful understanding statistical aspects of data.

HD

Apr 8, 2021

This is the best. It opens my eye for genomic data analysis.

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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