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 faz parte do Programa de cursos integrados Statistics with R
Linear Regression and Modeling
oferecido por
Informações sobre o curso
4.7
930 classificações
Habilidades que você terá
StatisticsLinear RegressionR ProgrammingRegression Analysis
Programa - O que você aprenderá com este curso
Semana
122 minutos para concluir
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 vídeo (total de (Total 2 mín.) min), 2 leituras
1 vídeos
2 leituras
About Statistics with R Specialization10min
More about Linear Regression and Modeling10min
2 horas para concluir
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 vídeos (total de (Total 47 mín.) min), 3 leituras, 2 testes
8 videos
Introduction58s
Correlation9min
Residuals1min
Least Squares Line11min
Prediction and Extrapolation3min
Conditions for Linear Regression10min
R Squared4min
Regression with Categorical Explanatory Variables5min
3 leituras
Lesson Learning Objectives10min
Lesson Learning Objectives10min
Week 1 Suggested Readings and Practice10min
2 exercícios práticos
Week 1 Practice Quiz8min
Week 1 Quiz18min
Semana
22 horas para concluir
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 vídeos (total de (Total 24 mín.) min), 5 leituras, 3 testes
5 leituras
Lesson Learning Objectives10min
Week 2 Suggested Readings and Exercises10min
About Lab Choices10min
Week 1 & 2 Lab Instructions (RStudio)10min
Week 1 & 2 Lab Instructions (RStudio Cloud)10min
3 exercícios práticos
Week 2 Practice Quiz6min
Week 2 Quiz16min
Week 1 & 2 Lab20min
Semana
33 horas para concluir
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 vídeos (total de (Total 57 mín.) min), 5 leituras, 3 testes
7 videos
Introduction2min
Multiple Predictors11min
Adjusted R Squared10min
Collinearity and Parsimony3min
Inference for MLR11min
Model Selection11min
Diagnostics for MLR7min
5 leituras
Lesson Learning Objectives10min
Lesson Learning Objectives10min
Week 3 Suggested Readings and Exercises10min
Week 3 Lab Instructions (RStudio)10min
Week 3 Lab Instructions (RStudio Cloud)10min
3 exercícios práticos
Week 3 Practice Quiz16min
Week 3 Quiz20min
Week 3 Lab20min
Semana
42 horas para concluir
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 leitura, 1 teste
1 leituras
Project Files and Rubric10min
33%
comecei uma nova carreira após concluir estes cursos
47%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
Melhores avaliações
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.
fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.\n\nthough you need to complete the prior courses to understand this.
Instrutores
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
Sobre Universidade 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.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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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