Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ. This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions — vital tasks with any type of regression.
You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide. Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy.
Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too.
By the end of this course, you will be able to: Explain when it is valid to use logistic regression Define odds and odds ratios Run simple and multiple logistic regression analysis in R and interpret the output Evaluate the model assumptions for multiple logistic regression in R Describe and compare some common ways to choose a multiple regression model This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation.
If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health. We hope you enjoy the course! The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression.
There will be mini-quizzes on the videos and the R exercises with feedback along the way to check your understanding. You will need basic numeracy for example, we will not use calculus and familiarity with graphical and tabular ways of presenting results. The three previous courses in the series explained concepts such as hypothesis testing, p values, confidence intervals, correlation and regression and showed how to install R and run basic commands.
In this course, we will recap all these core ideas in brief, but if you are unfamiliar with them, then you may prefer to take the first course in particular, Statistical Thinking in Public Health, and perhaps also the second, on linear regression, before embarking on this one. To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free.
Visit your learner dashboard to track your progress. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device. This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit.
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Check with your institution to learn more. We recommend taking the courses in the order in which they are displayed on the main page of the Specialization. More questions? Visit the Learner Help Center. Browse Chevron Right. Health Chevron Right. Public Health. Offered By.
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Graduation Cap. About this Specialization 32, recent views. Flexible Schedule. Flexible Schedule Set and maintain flexible deadlines. Beginner Level. Beginner Level Familiarity with seeing graphs and tables. Hours to complete. Available languages. English Subtitles: English. What you will learn Check Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice.
Using R for Statistics in Medical Research [BST02]
Check Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software. Check Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R. Check Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results. Chevron Left. How the Specialization Works.
Take Courses A Coursera Specialization is a series of courses that helps you master a skill. Hands-on Project Every Specialization includes a hands-on project.
Earn a Certificate When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network. There are 4 Courses in this Specialization. Course 1. Show All.
Course 2. Course 3. Main topics covered during this course will be: Basics of the R language, data accessing, data manipulations, explore and summarise your data using descriptive statistics, graphics in R, data analysis with focus on basic statistics e. Additional topics can be introduced, depending on the need and interest of participants e. During this course, a variety of teaching methods including lectures, exercises and interactive group discussions will be applied.
Mandatory home assignment will complete the learning portfolio.
Please bring your own notebook with an installation of R, available from www. This course is open for everyone who has to work with data and wants to learn how to use R. It is especially appropriate for clinical investigators and research personnel involved in the statistical analysis of clinical research projects. The Department of Clinical Research at the University of Basel is supporting members of registered clinical research groups by granting reduced course fees. If applicable, please provide the name of your clinical research group leader in the application form to be considered for a grant.
R-software: A Newer Tool in Epidemiological Data Analysis
Cancellation of your registration is possible until 10 days before the course date. Cancellation thereafter or absence from the course will result in a cancelation fee of half the course fee. Weiterbildungen Departement Klinische Forschung.