Statistics play a critical role in modern public health research and practice. This specialisation, offered by Imperial College London, equips learners with the essential skills to analyze and interpret data using the versatile R software. Through four comprehensive courses, participants will delve into statistical thinking, linear regression, logistic regression, and survival analysis in the context of public health. The program assumes no prior knowledge of statistics or R software, making it accessible to those with an interest in medical matters and quantitative data.
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This specialisation comprises four courses: Introduction to Statistics & Data Analysis in Public Health, Linear Regression in R for Public Health, Logistic Regression in R for Public Health, and Survival Analysis in R for Public Health. Participants will gain in-depth knowledge and practical skills in statistical analysis using R software.
Introduction to Statistics & Data Analysis in Public Health provides a foundation in statistical thinking and data analysis, covering key concepts such as descriptive statistics, graphical methods, and statistical associations between variables. Participants will learn to recognize and interpret statistical output and understand the role of chance and bias in results.
Linear Regression in R for Public Health focuses on the appropriate use of linear regression models in public health research. Participants will learn to analyze and interpret data using R, fit multiple linear regression models, and assess model assumptions.
Logistic Regression in R for Public Health delves into advanced analysis using R software, covering descriptive statistics, multiple logistic regression analysis, and evaluation of model assumptions. Participants will gain practical skills in interpreting and evaluating the output of logistic regression analysis.
Survival Analysis in R for Public Health explores Kaplan-Meier plots, Cox regression, and multiple regression model selection. Participants will learn to describe and compare common ways to choose a multiple regression model, providing them with a comprehensive understanding of survival analysis in the context of public health.
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