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John Hopkins University - Simple Regression Analysis in Public Health 

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Simple Regression Analysis in Public Health
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Overview

Duration

14 hours

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Total fee

Free

Mode of learning

Online

Difficulty level

Beginner

Official Website

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Credential

Certificate

Simple Regression Analysis in Public Health
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Highlights

  • This Course Plus the Full Specialization.
  • Shareable Certificates.
  • Graded Programming Assignments.
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Simple Regression Analysis in Public Health
 at 
Coursera 
Course details

Skills you will learn
More about this course
  • Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.

Simple Regression Analysis in Public Health
 at 
Coursera 
Curriculum

Simple Regression Methods

Introduction

Simple Regression: An Overview

Simple Linear Regression with a Binary (or Nominal Categorical) Predictor

Simple Linear Regression with a Categorical Predictor

Simple Linear Regression with a Continuous Predictor

Simple Linear Regression Model: Estimating the Regression Equation?Accounting for Uncertainty in the Estimates

Measuring the Strength of a Linear Association

Additional Examples

Solutions to Linear Regression Practice Quiz

Quiz 1 Solution

Practice Quiz: Simple Linear Regression

Simple Linear Regression

Introduction

Simple Logistic Regression: An Overview

Simple Logistic Regression with a Binary (or Categorical) Predictor

Simple Logistic Regression with a Continuous Predictor

Simple Logistic Regression: Accounting for Uncertainty in the Estimates

Estimating Risk and Functions of Risk from Logistic Regression Results

Additional Examples

Solutions to Practice Quiz on Simple Logistic Regression

Quiz 2 Solutions

Practice Quiz: Simple Logistic Regression

Logistic Regression

Simple Cox Proportional Hazards Regression

Introduction

Simple Cox Regression: An Overview

Simple Cox Regression with a Binary (or Categorical) Predictor

Simple Cox Regression with a Continuous Predictor

Simple Cox Regression: Accounting for Uncertainty in the Estimates

Estimating Survival Curves from Cox Regression Results

Additional Examples

Solutions for Practice Quiz on Simple Cox Regression

Solutions to Summative Quiz 3

Practice Quiz: Simple Cox Proportional Hazards Regression

Simple Cox Regression

Confounding, Adjustment, and Effect Modification

Introduction

Confounding: A Formal Definition and Some Examples

Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding

Adjusted Estimates: The General Idea Behind the Computations

Additional Examples

Effect modification: Introduction with an analytic example

Effect modification: Tree damage and elevation example

Effect modification: Examples from the literature

Confounding versus effect modification: A review

Additional examples

Solutions to Summative Quiz 4

Practice Quiz: Confounding and Effect Modification

Confounding and Effect Modification

Course Project

Biostatistical Consulting Project

Course Project Quiz

Simple Regression Analysis in Public Health
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Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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