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.
Este curso forma parte de Programa Especializado - Statistics with R
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Programa Especializado - Statistics with RUniversidad DukeAcerca de este Curso
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Resultados profesionales del estudiante
33%
comenzó una nueva carrera después de completar estos cursos
45%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 5 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano
Habilidades que obtendrás
StatisticsLinear RegressionR ProgrammingRegression Analysis
Resultados profesionales del estudiante
33%
comenzó una nueva carrera después de completar estos cursos
45%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 5 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano
ofrecido por
Universidad 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.
Programa - Qué aprenderás en este curso
22 minutos para completar
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!
22 minutos para completar
1 video (Total 2 minutos), 2 lecturas
1 video
2 lecturas
About Statistics with R Specialization10m
More about Linear Regression and Modeling10m
2 horas para completar
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.
2 horas para completar
8 videos (Total 47 minutos), 3 lecturas, 2 cuestionarios
8 videos
Introduction58s
Correlation9m
Residuals1m
Least Squares Line11m
Prediction and Extrapolation3m
Conditions for Linear Regression10m
R Squared4m
Regression with Categorical Explanatory Variables5m
3 lecturas
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice10m
2 ejercicios de práctica
Week 1 Practice Quiz8m
Week 1 Quiz18m
2 horas para completar
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!
2 horas para completar
3 videos (Total 24 minutos), 5 lecturas, 3 cuestionarios
5 lecturas
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 ejercicios de práctica
Week 2 Practice Quiz6m
Week 2 Quiz16m
Week 1 & 2 Lab20m
3 horas para completar
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!
3 horas para completar
7 videos (Total 57 minutos), 5 lecturas, 3 cuestionarios
7 videos
Multiple Predictors11m
Adjusted R Squared10m
Collinearity and Parsimony3m
Inference for MLR11m
Model Selection11m
Diagnostics for MLR7m
5 lecturas
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 ejercicios de práctica
Week 3 Practice Quiz16m
Week 3 Quiz20m
Week 3 Lab20m
2 horas para completar
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.
2 horas para completar
1 lectura
1 lectura
Project Files and Rubric10m
Revisiones
Principales revisiones sobre LINEAR REGRESSION AND MODELING
por PK23 de may. de 2017
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.
por DS29 de mar. de 2017
This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.
por EB25 de feb. de 2017
Good, but a little "smaller" than the Inferential statistics course (which is very complete). I would have liked to also learn Logistics regression, which I now have to learn elsewhere.
por LH14 de may. de 2020
It has been a great adventure so far. I still greatly appreciate how final projects are constructed that gives us freedom to choose our approach to the problems within the data set.
Acerca de Programa especializado: 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.
Preguntas Frecuentes
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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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