Acerca de este Curso

164,103 vistas recientes

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.
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.
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

Logotipo de Universidad Duke

Universidad Duke

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up94%(3,264 calificaciones)Info
Semana
1

Semana 1

22 minutos para completar

About Linear Regression and Modeling

22 minutos para completar
1 video (Total 2 minutos), 2 lecturas
2 lecturas
About Statistics with R Specialization10m
More about Linear Regression and Modeling10m
2 horas para completar

Linear Regression

2 horas para completar
8 videos (Total 47 minutos), 3 lecturas, 2 cuestionarios
8 videos
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
Semana
2

Semana 2

2 horas para completar

More about Linear Regression

2 horas para completar
3 videos (Total 24 minutos), 5 lecturas, 3 cuestionarios
3 videos
Inference for Linear Regression11m
Variability Partitioning5m
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
Semana
3

Semana 3

3 horas para completar

Multiple Regression

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
Semana
4

Semana 4

2 horas para completar

Final Project

2 horas para completar
1 lectura
1 lectura
Project Files and Rubric10m

Revisiones

Principales revisiones sobre LINEAR REGRESSION AND MODELING

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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....
Statistics with R

Preguntas Frecuentes

  • 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.

  • 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.

  • 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.

  • 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.

  • 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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