Об этом курсе: 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.
Автор: Johns Hopkins University
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public HealthПреподаватели: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthПреподаватели: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Основные сведения | Курс 7 из 10 — Data Science Specialization |
| Язык | Английский, Субтитры: Вьетнамский |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
9 видео, 11 материала для самостоятельного изучения
- 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
- Видео: Introduction: Basic Least Squares
- Reading: Technical details
- Видео: Technical Details (Skip if you'd like)
- Видео: Introductory Data Example
- Reading: Least squares
- Видео: Notation and Background
- Видео: Linear Least Squares
- Видео: Linear Least Squares Coding Example
- Видео: Technical Details (Skip if you'd like)
- Reading: Regression to the mean
- Видео: Regression to the Mean
- Reading: Practical R Exercises in swirl Part 1
- 採点対象外のプログラミング: swirl Lesson 1: Introduction
- 採点対象外のプログラミング: swirl Lesson 2: Residuals
- 採点対象外のプログラミング: swirl Lesson 3: Least Squares Estimation
Оцениваемый: Quiz 1
НЕДЕЛЯ 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 видео, 5 материала для самостоятельного изучения
- Reading: *Statistical* linear regression models
- Видео: Interpreting Coefficients
- Видео: Linear Regression for Prediction
- Reading: Residuals
- Видео: Residuals
- Видео: Residuals, Coding Example
- Видео: Residual Variance
- Reading: Inference in regression
- Видео: Inference in Regression
- Видео: Coding Example
- Видео: Prediction
- Reading: Looking ahead to the project
- Видео: Really, really quick intro to knitr
- Reading: Practical R Exercises in swirl Part 2
- 採点対象外のプログラミング: swirl Lesson 1: Residual Variation
- 採点対象外のプログラミング: swirl Lesson 2: Introduction to Multivariable Regression
- 採点対象外のプログラミング: swirl Lesson 3: MultiVar Examples
Оцениваемый: Quiz 2
НЕДЕЛЯ 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 видео, 5 материала для самостоятельного изучения, 1 тренировочных теста
- Reading: Multivariable regression
- Видео: Multivariable Regression part II
- Видео: Multivariable Regression Continued
- Видео: Multivariable Regression Examples part I
- Видео: Multivariable Regression Examples part II
- Видео: Multivariable Regression Examples part III
- Видео: Multivariable Regression Examples part IV
- Reading: Adjustment
- Видео: Adjustment Examples
- Reading: Residuals
- Видео: Residuals and Diagnostics part I
- Видео: Residuals and Diagnostics part II
- Видео: Residuals and Diagnostics part III
- Reading: Model selection
- Видео: Model Selection part I
- Видео: Model Selection part II
- Видео: Model Selection part III
- Reading: Practical R Exercises in swirl Part 3
- 採点対象外のプログラミング: swirl Lesson 1: MultiVar Examples2
- 採点対象外のプログラミング: swirl Lesson 2: MultiVar Examples3
- 採点対象外のプログラミング: swirl Lesson 3: Residuals Diagnostics and Variation
- 練習問題: (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)
Оцениваемый: Quiz 3
НЕДЕЛЯ 4
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
7 видео, 6 материала для самостоятельного изучения
- Reading: GLMs
- Reading: Logistic regression
- Видео: Logistic Regression part I
- Видео: Logistic Regression part II
- Видео: Logistic Regression part III
- Reading: Count Data
- Видео: Poisson Regression part I
- Видео: Poisson Regression part II
- Reading: Mishmash
- Видео: Hodgepodge
- Reading: Practical R Exercises in swirl Part 4
- 採点対象外のプログラミング: swirl Lesson 1: Variance Inflation Factors
- 採点対象外のプログラミング: swirl Lesson 2: Overfitting and Underfitting
- 採点対象外のプログラミング: swirl Lesson 3: Binary Outcomes
- 採点対象外のプログラミング: swirl Lesson 4: Count Outcomes
- Reading: Post-Course Survey
Оцениваемый: Quiz 4
Оцениваемый: Regression Models Course Project
Часто задаваемые вопросы
Как это работает
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Авторы
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.
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Рейтинги и отзывы
a very good course before digging deeper into Data Science advanced topics.
Excellent course
This course was a great as an intro to regression models, material was good but needs some update on the links, for the structure of topics it would be better if it was more coherent as many topics were covered randomly in different weeks like residuals.
Thanks for the instructor Brian Caffo for the good material and and clarification of concepts for a better understanding for students.
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PD
Impressive! Very detailed in statistics and Mathematics, I would like an extensive course in logistic regression, it was short compared with lm course.