Adjusting for Covariates (4:16) - Module 2 | Coursera

Adjusting for Covariates (4:16)

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基因组数据科学所需的统计学

约翰霍普金斯大学

4.2（328 个评分）

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31K 名学生已注册

课程 6（共 6 门，基因组数据科学专项课程）

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您将学习的技能

Statistics, Data Analysis, R Programming, Biostatistics

审阅

4.2（328 个评分）

5 stars
54.26%
4 stars
26.82%
3 stars
11.58%
2 stars
2.43%
1 star
4.87%

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.

从本节课中

Module 2

This week we will cover preprocessing, linear modeling, and batch effects.

Module 2 Overview (1:12)1:12

Dimension Reduction (12:13)12:13

Dimension Reduction (in R) (8:48)8:48

Pre-processing and Normalization (11:26)11:26

Quantile Normalization (in R) (4:49)4:49

The Linear Model (6:50)6:50

Linear Models with Categorical Covariates (4:08)4:08

Adjusting for Covariates (4:16)4:16

Linear Regression in R (13:03)13:03

Many Regressions at Once (3:50)3:50

Many Regressions in R (7:21)7:21

Batch Effects and Confounders (7:11)7:11

Batch Effects in R: Part A (8:18)8:18

Batch Effects in R: Part B (3:50)3:50

教学方

Jeff Leek, PhD
Associate Professor, Biostatistics

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