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Statistical Thinking in Epidemiology [Yu-Kang Tu, Mark S. Gilthorpe] on Amazon. com. *FREE* shipping on qualifying offers. While biomedical researchers may.
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Should any reader come to this text thinking that the interpretation of regression results is a simple matter, they will be quickly disabused.

Statistical Thinking in Epidemiology - Yu-Kang Tu, Mark S. Gilthorpe - كتب Google

First trained as a dentist and then an epidemiologist, he has published extensively in dental, medical, epidemiological and statistical journals. He is interested in developing statistical methodologies to solve statistical and methodological problems such as mathematical coupling, regression to the mean, collinearity and the reversal paradox. His current research focuses on applying latent variables methods, e. More recently, he has been working on applying partial least squares regression to epidemiological data. Having completed a single honours degree in mathematical Physics University of Nottingham , he undertook a PhD in Mathematical Modelling University of Aston in Birmingham , before initially embarking upon a career as self-employed Systems and Data Analyst and Computer Programmer, and eventually becoming an academic in biomedicine.

His research focus has persistently been that of the development and promotion of robust and sophisticated modelling methodologies for non-experimental and sometimes large and complex observational data within biomedicine, leading to extensive publications in dental, medical, epidemiological and statistical journals. Routledge eBooks are available through VitalSource. An eBook version of this title already exists in your shopping cart.

If you would like to replace it with a different purchasing option please remove the current eBook option from your cart. Statistical Thinking in Epidemiology 1st Edition.

For Instructors Request Inspection Copy. Paperback : Hardback : Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. 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 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. Familiarity with seeing graphs and tables. Basic numeracy so NOT calculus, trigonometry etc. No medical, statistical or R knowledge is assumed. Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice. Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software.

Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R. Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results. A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. Visit your learner dashboard to track your course enrollments and your progress.

Statistical Analysis with R for Public Health Specialization

Every Specialization includes a hands-on project. You'll need to successfully finish the project s to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it. 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. This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions.

You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality.

There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy for example, we will not use calculus and familiarity with graphical and tabular ways of presenting results.

No knowledge of R or programming is assumed. Knowing what causes disease and what makes it worse are clearly vital parts of this.


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This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function.

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.

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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.

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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.