Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
ジョンズ・ホプキンズ大学（Johns Hopkins University）このコースについて
127,413 最近の表示
受講生の就業成果
23%
コース終了後に新しいキャリアをスタートした
25%
コースが具体的なキャリアアップにつながった
14%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
約54時間で修了
英語
字幕：英語, 韓国語, ベトナム語
学習内容
Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing
習得するスキル
Model SelectionGeneralized Linear ModelLinear RegressionRegression Analysis
受講生の就業成果
23%
コース終了後に新しいキャリアをスタートした
25%
コースが具体的なキャリアアップにつながった
14%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
約54時間で修了
英語
字幕：英語, 韓国語, ベトナム語
提供：
ジョンズ・ホプキンズ大学（Johns Hopkins University）
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
シラバス - 本コースの学習内容
12時間で修了
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
12時間で修了
9件のビデオ （合計74分）, 11 readings, 4 quizzes
9件のビデオ
Introduction: Basic Least Squares6 分
Technical Details (Skip if you'd like)2 分
Introductory Data Example12 分
Notation and Background7 分
Linear Least Squares6 分
Linear Least Squares Coding Example7 分
Technical Details (Skip if you'd like)11 分
Regression to the Mean11 分
11件の学習用教材
Welcome to Regression Models10 分
Book: Regression Models for Data Science in R10 分
Syllabus10 分
Pre-Course Survey10 分
Data Science Specialization Community Site10 分
Where to get more advanced material10 分
Regression10 分
Technical details10 分
Least squares10 分
Regression to the mean10 分
Practical R Exercises in swirl Part 110 分
1の練習問題
Quiz 120 分
11時間で修了
Week 2: Linear Regression & Multivariable Regression
This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
11時間で修了
10件のビデオ （合計70分）, 5 readings, 4 quizzes
10件のビデオ
Interpreting Coefficients3 分
Linear Regression for Prediction10 分
Residuals5 分
Residuals, Coding Example14 分
Residual Variance7 分
Inference in Regression5 分
Coding Example6 分
Prediction9 分
Really, really quick intro to knitr3 分
5件の学習用教材
*Statistical* linear regression models10 分
Residuals10 分
Inference in regression10 分
Looking ahead to the project10 分
Practical R Exercises in swirl Part 210 分
1の練習問題
Quiz 220 分
13時間で修了
Week 3: Multivariable Regression, Residuals, & Diagnostics
This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
13時間で修了
14件のビデオ （合計168分）, 5 readings, 5 quizzes
14件のビデオ
Multivariable Regression part II10 分
Multivariable Regression Continued8 分
Multivariable Regression Examples part I19 分
Multivariable Regression Examples part II22 分
Multivariable Regression Examples part III7 分
Multivariable Regression Examples part IV7 分
Adjustment Examples17 分
Residuals and Diagnostics part I5 分
Residuals and Diagnostics part II9 分
Residuals and Diagnostics part III9 分
Model Selection part I7 分
Model Selection part II22 分
Model Selection part III12 分
5件の学習用教材
Multivariable regression10 分
Adjustment10 分
Residuals10 分
Model selection10 分
Practical R Exercises in swirl Part 310 分
2の練習問題
Quiz 314 分
(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)8 分
17時間で修了
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
17時間で修了
7件のビデオ （合計95分）, 6 readings, 6 quizzes
7件のビデオ
GLMs21 分
Logistic Regression part I17 分
Logistic Regression part II3 分
Logistic Regression part III8 分
Poisson Regression part I12 分
Poisson Regression part II12 分
Hodgepodge18 分
6件の学習用教材
GLMs10 分
Logistic regression10 分
Count Data10 分
Mishmash10 分
Practical R Exercises in swirl Part 410 分
Post-Course Survey10 分
1の練習問題
Quiz 412 分
レビュー
REGRESSION MODELS からの人気レビュー
KAにより2017年12月16日
Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.
BAにより2017年1月31日
It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.
DAにより2019年3月10日
This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!
AWにより2018年2月19日
Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.
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