This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
Ce cours fait partie de la Spécialisation Statistics with R
Linear Regression and Modeling
proposé par
À propos de ce cours
42,530 consultations récentes
Compétences que vous acquerrez
StatisticsLinear RegressionR ProgrammingRegression Analysis
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
122 minutes pour terminer
About Linear Regression and Modeling
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!
1 vidéo (Total 2 min), 2 lectures
1 vidéo
2 lectures
About Statistics with R Specialization10 min
More about Linear Regression and Modeling10 min
2 heures pour terminer
Linear Regression
In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.
8 vidéos (Total 47 min), 3 lectures, 2 quiz
8 vidéos
Introduction58s
Correlation9 min
Residuals1 min
Least Squares Line11 min
Prediction and Extrapolation3 min
Conditions for Linear Regression10 min
R Squared4 min
Regression with Categorical Explanatory Variables5 min
3 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Week 1 Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 1 Practice Quiz8 min
Week 1 Quiz18 min
Semaine
22 heures pour terminer
More about Linear Regression
Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!
3 vidéos (Total 24 min), 5 lectures, 3 quiz
3 vidéos
5 lectures
Lesson Learning Objectives10 min
Week 2 Suggested Readings and Exercises10 min
About Lab Choices10 min
Week 1 & 2 Lab Instructions (RStudio)10 min
Week 1 & 2 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 2 Practice Quiz6 min
Week 2 Quiz16 min
Week 1 & 2 Lab20 min
Semaine
33 heures pour terminer
Multiple Regression
In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. Hope you enjoy!
7 vidéos (Total 57 min), 5 lectures, 3 quiz
7 vidéos
Introduction2 min
Multiple Predictors11 min
Adjusted R Squared10 min
Collinearity and Parsimony3 min
Inference for MLR11 min
Model Selection11 min
Diagnostics for MLR7 min
5 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Week 3 Suggested Readings and Exercises10 min
Week 3 Lab Instructions (RStudio)10 min
Week 3 Lab Instructions (RStudio Cloud)10 min
3 exercices pour s'entraîner
Week 3 Practice Quiz16 min
Week 3 Quiz20 min
Week 3 Lab20 min
Semaine
42 heures pour terminer
Final Project
In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.
1 lecture, 1 quiz
1 lecture
Project Files and Rubric10 min
4.7
177 avis33%
a commencé une nouvelle carrière après avoir terminé ces cours
45%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
Principaux examens pour Linear Regression and Modeling
Very good course taught by Dr. Mine who is as always a very good teacher. The videos are very eloquent and easy to understand. Highly recommend it if you are looking for a basic refresher course.
I feel I'm running out of complement words for this course series. In conclusion, clear teaching, helpful project, and knowledgeable classmates that I can learn from through final project.
Enseignant
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
À propos de Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
À propos de la Spécialisation Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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