Summary Statistics in Public Health
- Offered byCoursera
Summary Statistics in Public Health at Coursera Overview
Duration | 14 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Summary Statistics in Public Health at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 4 in the Biostatistics in Public Health Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
- Approx. 14 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Summary Statistics in Public Health at Coursera Course details
- Biostatistics is the application of statistical reasoning to the life sciences, and it is the key to unlocking the data gathered by researchers and the evidence presented in the scientific literature. In this course, we'll focus on the use of statistical measurement methods within the world of public health research. Along the way, you'll be introduced to a variety of methods and measures, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include summary measures, visual displays, continuous data, sample size, the normal distribution, binary data, the element of time, and the Kaplan-Meir curve.
Summary Statistics in Public Health at Coursera Curriculum
The Role of Statistics in Public Health Research
Welcome
Introduction and Overview
Samples Versus Population
Considerations with Regard to Study Design
Data Types and Summarization
Additional Examples
Practice Quiz: The Role of Statistics in Public Health Research
Introduction
Useful Summary Statistics
Visual Displays
The Role of Sample Size on Sample Based Estimates
Comparing Distributions of Continuous Data
Additional Examples
Practice: Continuous Data Measures
Continuous Data Measures
The Normal Distribution
Introduction
The Standard Normal Distribution Defined
Applying the Principles of the Normal Distribution to Sample Data to Estimate Characteristics of Population Data
What Happens When We Apply the Properties of the Normal Distribution to Data Not Approximately Normal: A Warning
Additional Examples
Practice: The Normal Distribution
The Normal Distribution
Binary Data
Introduction
Binary Data: Definition and Summarization (Binomial Distribution, P-Hat, SD)
Comparing Distributions of Binary Data: Risk Difference and Relative Risk
Comparing Distributions of Binary Data: Odds Ratios
A Brief Note About Ratios (Will Also Be Revisited Later in the Course)
Additional Examples
Practice: Binary Data
Binary Data
Dealing with the Element of Time
Introduction
Time to Event Data: Definition (Censoring) and Numerical Summary Measures (Incidence Rates)
Numerically Comparing Groups on Time to Event Outcomes
Summarizing Time-to-Event Data, Graphically
Graphically Comparing Groups on Time to Event Outcomes
Additional Examples
Practice: Dealing with the Element of Time
Dealing with Elements of Time
Biostatistical Consulting Project
Course Project Quiz
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