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Biostatistics in Public Health

This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.

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What's inside

Four courses

Summary Statistics in Public Health

(0 hours)

Biostatistics, the application of statistical reasoning to the life sciences, is key to interpreting data in public health research. This course introduces statistical measurement methods used in public health, including summary measures, visual displays, continuous data, sample size, the normal distribution, binary data, the element of time, and the Kaplan-Meir curve.

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Hypothesis Testing in Public Health

(0 hours)

Biostatistics is essential for public health researchers, providing methods for extracting meaningful conclusions from data. This course covers sample variability, statistical hypothesis testing, calculations, and interpreting real-world data. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.

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Simple Regression Analysis in Public Health

(0 hours)

Biostatistics, the application of statistical reasoning to the life sciences, is key to understanding data in scientific public health literature. This course focuses on simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.

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Multiple Regression Analysis in Public Health

(0 hours)

Biostatistics applies statistical reasoning to the life sciences. This course extends simple regression to predicting a single outcome based on multiple variables. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.

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Learning objectives

Calculate summary statistics from public health and biomedical data
Interpret written and visual presentations of statistical data

Evaluate and interpret results of various regression methods
Choose the most appropriate statistical method to answer your research question

Calculate summary statistics from public health and biomedical data
Interpret written and visual presentations of statistical data
Evaluate and interpret results of various regression methods
Choose the most appropriate statistical method to answer your research question

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Time

0 hours

Level

Introductory

Via

Coursera

Institution

Johns Hopkins University

Instructor

John McGready, PhD, MS

Level

Introductory

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