Statistical Analysis with R for Public Health Specialization

Start Date: 02/23/2020

Course Type: Specialization Course

Course Link:

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About Course

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.

Course Syllabus

Introduction to Statistics & Data Analysis in Public Health
Linear Regression in R for Public Health
Logistic Regression in R for Public Health
Survival Analysis in R for Public Health

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Course Introduction

Master Statistics for Public Health and Learn R. Develop your statistical thinking skills and learn key data analysis methods through R Statistical Analysis with R for Public Health Specialization This course introduces the statistical concepts of linear regression and graphical displays of data in R. The course covers linear regression, logistic regression, normal regression, and interaction effects. A probabilistic view of the results is presented. Lasso and confidence intervals are also introduced. A variety of statistical tests are covered, such as two t-statistics, a p-value, ANOVA, and significance testing, and two two-tailed Wilcoxon signed-rank tests. These concepts are presented in an easy-to-follow and intuitive way that students can continue their studies at home. This course is the first step towards earning your bachelor's degree in Public Health. You will need to pass the prerequisite courses (Core course and Specialization on Quantitative Research in Public Health, and complete the Specialization “Introduction to Data Analysis”) to take this course. This course covers the following topics: Linear Regression: logistic, normal, and multiple regression Logistic Regression: lasso and Wilcoxon Multiple Regression: ANOVA and Wilcoxon P-values: two-tailed Wilcoxon, P-value, and significance testing Wilcoxon signed-rank test: Wilcoxon signed-rank test, ANOVA, significance testing, and two-tailed Wilcoxon signed-rank test This course is the second step towards earning your bachelor's degree in Public Health. You will need

Course Tag

Statistical Thinking Survival Analysis Logistic Regression Data analysis with R Linear Regression

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