This course will teach you the fundamental building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but it will also teach you how to take a data set you've never seen before, describe its key features, learn about its strengths and quirks, run some critical basic analyses, and then formulate and test hypotheses based on means and proportions. You'll learn how to use the popular, flexible, and completely free software R, which is used by statisticians and machine learning practitioners all over the world. Because it is hands-on, you will first learn how to phrase a testable hypothesis through examples of medical research as reported in the media.
In this course, you will learn :
- Defend statistics' critical role in modern public health research and practise.
- Using descriptive statistics and graphical methods in R, describe a data set from scratch, including data item features and data quality issues.
- In R, choose and apply appropriate methods for formulating and examining statistical associations between variables in a data set.
- Interpret the results of your analysis and evaluate the role of chance and bias.
1. Introduction to Statistics in Public Health
- Uses of Statistics in Public Health
- Introduction to Sampling
- How to Formulate a Research Question
- Formulating a research question for the Parkinson's disease and supplement studies
2. Types of Variables, Common Distributions and Sampling
- Overview of types of variables
- Well-behaved Distributions
- Real-world Distributions and their Problems
- The Role of Sampling in Public Health Research
- How to choose a Sample
3. Introduction to R and RStudio
- How to describe distributions of real data
- How to Load Data and run Basic Tabulations in R
4. Hypothesis Testing in R
- Sampling errors for proportions and central limit theorem
- Hypothesis Testing
- Choosing the Sample Size for your Study