Acerca de este Curso

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 intermedio

You'll need to have taken the Statistical Thinking and Linear Regression courses in this series or have equivalent knowledge.

Aprox. 9 horas para completar

Sugerido: 3-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software

  • Check

    Interpret the output from your analysis and appraise the role of chance and bias as potential explanations

  • Check

    Run multiple logistic regression analysis in R and interpret the output

  • Check

    Evaluate the model assumptions for multiple logistic regression in R

Habilidades que obtendrás

Logistic RegressionR Programming

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 intermedio

You'll need to have taken the Statistical Thinking and Linear Regression courses in this series or have equivalent knowledge.

Aprox. 9 horas para completar

Sugerido: 3-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
2 horas para completar

Introduction to Logistic Regression

Welcome to Statistics for Public Health: Logistic Regression for Public Health! In this week, you will be introduced to logistic regression and its uses in public health. We will focus on why linear regression does not work with binary outcomes and on odds and odds ratios, and you will finish the week by practising your new skills. By the end of this week, you will be able to explain when it is valid to use logistic regression, and define odds and odds ratios. Good luck!...
3 videos (Total 12 minutos), 7 readings, 2 quizzes
3 videos
Introduction to Logistic Regression5m
Odds and Odds Ratios3m
7 lecturas
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Data set and Glossary10m
Additional Reading10m
Why does linear regression not work with binary outcomes?10m
Odds Ratios and Examples from the Literature10m
2 ejercicios de práctica
Logistic Regression10m
End of Week Quiz10m
Semana
2
3 horas para completar

Logistic Regression in R

In this week, you will learn how to prepare data for logistic regression, how to describe data in R, how to run a simple logistic regression model in R, and how to interpret the output. You will also have the opportunity to practise your new skills. By the end of this week, you will be able to run simple logistic regression analysis in R and interpret the output. Good luck! ...
2 videos (Total 11 minutos), 4 readings, 2 quizzes
2 videos
Logistic Regression in R5m
4 lecturas
How to Describe Data in R20m
Results of Cross-tabulation20m
Practice in R: Simple Logistic Regression15m
Feedback: Output and Interpretation from Simple Logistic Regression35m
2 ejercicios de práctica
Cross Tabulation30m
Interpreting Simple Logistic Regression30m
Semana
3
3 horas para completar

Running Multiple Logistic Regression in R

Now that you're happy with including one predictor in the model, this week you'll learn how to run multiple logistic regression, including describing and preparing your data and running new logistic regression models. You will have the opportunity to practise your new skills. By the end of the week, you will be able to run multiple logistic regression analysis in R and interpret the output. Good luck!...
1 video (Total 4 minutos), 6 readings, 1 quiz
6 lecturas
Describing your Data and Preparing to Run Multiple Logistic Regression35m
Practice in R: Describing Variables20m
Feedback20m
Practice in R: Running Multiple Logistic Regression15m
Feedback: Multiple Regression Model10m
Feedback on the Assessment10m
1 ejercicio de práctica
Running A New Logistic Regression Model30m
Semana
4
5 horas para completar

Assessing Model Fit

Welcome to the final week of the course! In this week, you will learn how to assess model fit and model performance, how to avoid the problem of overfitting, and how to choose what variables from your data set should go into your multiple regression model. You will put all the skills you have learned throughout the course into practice. By the end of this week, you will be able to evaluate the model assumptions for multiple logistic regression in R, and describe and compare some common ways to choose a multiple regression model. Good luck! ...
3 videos (Total 17 minutos), 10 readings, 3 quizzes
3 videos
Overfitting and Non-convergence6m
Summary of the Course3m
10 lecturas
Model Fit in Logistic Regression10m
How to Interpret Model Fit and Performance Information in R10m
Further Reading on Model Fit20m
Summary of Different Ways to Run Multiple Regression10m
Practice in R: Applying Backwards Elimination30m
Feedback: Backwards Elimination20m
Practice in R: Run a Model with Different Predictors30m
Feedback on the New Model10m
Further Reading on Model Selection Methods20m
R Code for the Whole Module20m
3 ejercicios de práctica
Quiz on R’s Default Output for the Model30m
Overfitting and Model Selection20m
End of Course Quiz

Instructor

Avatar

Alex Bottle

Reader in Medical Statistics
School of Public Health

Acerca de Imperial College London

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

Acerca del programa especializado Statistical Analysis with R for Public Health

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....
Statistical Analysis with R for Public Health

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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