Welcome to Introduction to Statistics & Data Analysis in Public Health! This course offers a comprehensive understanding of statistical analysis in the realm of public health. Through hands-on learning with the popular software R, students will explore the core building blocks of statistical analysis, types of variables, common distributions, and hypothesis testing. The course equips learners to describe and analyze data sets, formulate and test hypotheses, and interpret the findings critically. With a focus on real-world public health data, students will gain valuable skills to navigate the complexities of modern research and practice.
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This course is divided into four modules. Module 1 introduces statistical thinking in public health, Module 2 covers types of variables and common distributions, Module 3 focuses on introduction to R and RStudio, and Module 4 delves into hypothesis testing in R.
Welcome to Module 1: Introduction to Statistics in Public Health. This module provides an overview of statistical thinking in public health and introduces the uses of statistics in the field. Students will also learn about sampling, formulating research questions, and be familiarized with data sets and glossary.
Module 2: Types of Variables, Common Distributions and Sampling, delves into the types of variables, distributions, and the role of sampling in public health research. Students will understand how to choose a sample, estimate population mean, and deal with real-world data distributions and their problems.
Module 3: Introduction to R and RStudio introduces students to the practical application of R for statistical analysis. Learners will understand how to load data, perform basic tabulations, calculate percentiles, and describe distributions of real data using R.
Module 4: Hypothesis Testing in R focuses on sampling errors, hypothesis testing, choosing sample sizes, and comparing means using R. Students will also gain insights into the chi-squared test and learn to interpret the results of hypothesis testing in R.
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