À propos de ce cours : 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.
Créé par : Johns Hopkins University
Enseigné par : Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Informations de base | Cours 7 de 10 dans Data Science Specialization |
| Langue | English, Sous-titres : Vietnamese |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
9 vidéos, 11 lectures
- Reading: Welcome to Regression Models
- Reading: Book: Regression Models for Data Science in R
- Reading: Syllabus
- Reading: Pre-Course Survey
- Reading: Data Science Specialization Community Site
- Reading: Where to get more advanced material
- Reading: Regression
- Vidéo: Introduction: Basic Least Squares
- Reading: Technical details
- Vidéo: Technical Details (Skip if you'd like)
- Vidéo: Introductory Data Example
- Reading: Least squares
- Vidéo: Notation and Background
- Vidéo: Linear Least Squares
- Vidéo: Linear Least Squares Coding Example
- Vidéo: Technical Details (Skip if you'd like)
- Reading: Regression to the mean
- Vidéo: Regression to the Mean
- Reading: Practical R Exercises in swirl Part 1
- Ungraded Programming: swirl Lesson 1: Introduction
- Ungraded Programming: swirl Lesson 2: Residuals
- Ungraded Programming: swirl Lesson 3: Least Squares Estimation
Noté: Quiz 1
SEMAINE 2
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.
10 vidéos, 5 lectures
- Reading: *Statistical* linear regression models
- Vidéo: Interpreting Coefficients
- Vidéo: Linear Regression for Prediction
- Reading: Residuals
- Vidéo: Residuals
- Vidéo: Residuals, Coding Example
- Vidéo: Residual Variance
- Reading: Inference in regression
- Vidéo: Inference in Regression
- Vidéo: Coding Example
- Vidéo: Prediction
- Reading: Looking ahead to the project
- Vidéo: Really, really quick intro to knitr
- Reading: Practical R Exercises in swirl Part 2
- Ungraded Programming: swirl Lesson 1: Residual Variation
- Ungraded Programming: swirl Lesson 2: Introduction to Multivariable Regression
- Ungraded Programming: swirl Lesson 3: MultiVar Examples
Noté: Quiz 2
SEMAINE 3
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.
14 vidéos, 5 lectures, 1 quiz pour s'exercer
- Reading: Multivariable regression
- Vidéo: Multivariable Regression part II
- Vidéo: Multivariable Regression Continued
- Vidéo: Multivariable Regression Examples part I
- Vidéo: Multivariable Regression Examples part II
- Vidéo: Multivariable Regression Examples part III
- Vidéo: Multivariable Regression Examples part IV
- Reading: Adjustment
- Vidéo: Adjustment Examples
- Reading: Residuals
- Vidéo: Residuals and Diagnostics part I
- Vidéo: Residuals and Diagnostics part II
- Vidéo: Residuals and Diagnostics part III
- Reading: Model selection
- Vidéo: Model Selection part I
- Vidéo: Model Selection part II
- Vidéo: Model Selection part III
- Reading: Practical R Exercises in swirl Part 3
- Ungraded Programming: swirl Lesson 1: MultiVar Examples2
- Ungraded Programming: swirl Lesson 2: MultiVar Examples3
- Ungraded Programming: swirl Lesson 3: Residuals Diagnostics and Variation
- Practice Quiz: (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)
Noté: Quiz 3
SEMAINE 4
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
7 vidéos, 6 lectures
- Reading: GLMs
- Reading: Logistic regression
- Vidéo: Logistic Regression part I
- Vidéo: Logistic Regression part II
- Vidéo: Logistic Regression part III
- Reading: Count Data
- Vidéo: Poisson Regression part I
- Vidéo: Poisson Regression part II
- Reading: Mishmash
- Vidéo: Hodgepodge
- Reading: Practical R Exercises in swirl Part 4
- Ungraded Programming: swirl Lesson 1: Variance Inflation Factors
- Ungraded Programming: swirl Lesson 2: Overfitting and Underfitting
- Ungraded Programming: swirl Lesson 3: Binary Outcomes
- Ungraded Programming: swirl Lesson 4: Count Outcomes
- Reading: Post-Course Survey
Noté: Quiz 4
Noté: Regression Models Course Project
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Great course with great materials. Easy to understand and to learn.
Overall a good course. But I was expecting more in depth covering of the topics.
I found this part of the course one of the hardest. But at the same time, probably the best course about linear regression I have ever seen. Is it difficult? Yes! Is it super exciting? Well... :) not necessarily. But I have come back to these course materials many times for a good reason. It's what you need to understand and use all the time. It is the absolute essential and necessary to know for any data scientist. While it might appear to be boring and basic compared to fancy deep learning models... trust me. It's not. It goes a long way in understanding what can be done with data. I am very grateful to the course instructors that they have spent their time and effort to make this course what it is. Please keep up the good work!
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