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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Statistics with R
Linear Regression and Modeling
von
Über diesen Kurs
46,249 kürzliche Aufrufe
Kompetenzen, die Sie erwerben
StatisticsLinear RegressionR ProgrammingRegression Analysis
Kursteilnehmer, die sich für Course entscheiden, sind
- Economists
- Data Scientists
- Data Analysts
- Biostatisticians
- Biologists
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
122 Minuten zum Abschließen
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 Video (Gesamt 2 min), 2 Lektüren
1 Video
2 Lektüren
About Statistics with R Specialization10m
More about Linear Regression and Modeling10m
2 Stunden zum Abschließen
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 Videos (Gesamt 47 min), 3 Lektüren, 2 Quiz
8 Videos
Correlation9m
Residuals1m
Least Squares Line11m
Prediction and Extrapolation3m
Conditions for Linear Regression10m
R Squared4m
Regression with Categorical Explanatory Variables5m
3 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice10m
2 praktische Übungen
Week 1 Practice Quiz8m
Week 1 Quiz18m
Woche
22 Stunden zum Abschließen
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 Videos (Gesamt 24 min), 5 Lektüren, 3 Quiz
5 Lektüren
Lesson Learning Objectives10m
Week 2 Suggested Readings and Exercises10m
About Lab Choices10m
Week 1 & 2 Lab Instructions (RStudio)10m
Week 1 & 2 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 2 Practice Quiz6m
Week 2 Quiz16m
Week 1 & 2 Lab20m
Woche
33 Stunden zum Abschließen
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 Videos (Gesamt 57 min), 5 Lektüren, 3 Quiz
7 Videos
Multiple Predictors11m
Adjusted R Squared10m
Collinearity and Parsimony3m
Inference for MLR11m
Model Selection11m
Diagnostics for MLR7m
5 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Exercises10m
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 3 Practice Quiz16m
Week 3 Quiz20m
Week 3 Lab20m
Woche
42 Stunden zum Abschließen
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 Lektüre, 1 Quiz
1 Lektüre
Project Files and Rubric10m
33%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
45%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
12%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Top-Bewertungen von 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.
Dozent
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
Über Duke University
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.
Über den Spezialisierung 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Wie erfolgen Rückerstattungen?
Ist finanzielle Unterstützung möglich?
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.
Haben Sie weitere Fragen? Besuchen Sie das Hilfe-Center für Teiln..