About this Course
최근 조회 16,194

다음 전문 분야의 3개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

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

영어

자막: 영어

배울 내용

  • 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

귀하가 습득할 기술

Logistic RegressionR Programming

다음 전문 분야의 3개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

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 min), 7 readings, 2 quizzes
7개의 읽기 자료
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
2개 연습문제
Logistic Regression10m
End of Week Quiz10m
2
완료하는 데 3시간 필요

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 min), 4 readings, 2 quizzes
4개의 읽기 자료
How to Describe Data in R20m
Results of Cross Tabulation20m
Practice in R: Simple Logistic Regression15m
Feedback: Output and Interpretation from Simple Logistic Regression35m
2개 연습문제
Cross Tabulation30m
Interpreting Simple Logistic Regression30m
3
완료하는 데 3시간 필요

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 min), 6 readings, 1 quiz
6개의 읽기 자료
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
1개 연습문제
Running A New Logistic Regression Model30m
4
완료하는 데 5시간 필요

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 min), 10 readings, 3 quizzes
10개의 읽기 자료
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
3개 연습문제
Quiz on R’s Default Output for the Model30m
Overfitting and Model Selection20m
End of Course Quiz
4.7
3개의 리뷰Chevron Right

Logistic Regression in R for Public Health의 최상위 리뷰

대학: MAApr 1st 2019

This is one of the best courses. Dr. Alex is amazing and delivers the content quite well.

강사

Avatar

Alex Bottle

Reader in Medical Statistics
School of Public Health

Start working towards your Master's degree

이 강좌은(는) 임페리얼 칼리지 런던의 100% 온라인 Global Master of Public Health 중 일부입니다. 전체 프로그램을 수료하면 귀하의 강좌가 학위 취득에 반영됩니다.

임페리얼 칼리지 런던 정보

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....

공중 보건학을 위한 R을 통한 통계 분석 전문 분야 정보

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....
공중 보건학을 위한 R을 통한 통계 분석

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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