University of Michigan - Logistic Regression and Prediction for Health Data
- Offered byCoursera
Logistic Regression and Prediction for Health Data at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Logistic Regression and Prediction for Health Data at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 7 quizzes
Logistic Regression and Prediction for Health Data at Coursera Course details
- What you'll learn
- Understand how binary outcomes arise and know the difference between prevalence, risk ratios, and odds ratios
- Use logistic regression to estimate and interpret the association between one or more predictors and a binary outcome
- Understand the principles for using logistic regression to make predictions and assessing the quality of those predictions
- This course introduces learners to the analysis of binary/dichotomous outcomes
- Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression
- They will understand the connection between prevalence, risk ratios, and odds ratios
- By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises
Logistic Regression and Prediction for Health Data at Coursera Curriculum
Simple Comparisons of Binary Outcomes
Data Science for Health Research: Specialization Introduction
How and When Binary Outcomes Can Arise
A Need for Models Beyond Linear Regression
Binary Outcomes, Comparing Between Two Groups (Part 1)
Binary Outcomes, Comparing Between Two groups (part 2)
Binary Outcomes, Comparing Between Two groups (part 3)
Guided Practice: Z-Test
Guided Practice: Fisher's Exact Test
Analyzing a Binary Outcome and Binary Exposure with the Odds Ratio
Interpreting the Odds Ratio
2x2 Example: The WCGS Cardiovascular Study
Meet Your Instructors
Welcome & Course Syllabus
Pre-Course Survey
Introduction To and How To Use Independent Guides
Introduction to the BPUrban Data
1.2 Independent Guide
1.2 Discussion Prompt Suggested Answer
End of Module 1 Discussion Prompt Suggested Answer
1.2 Practice Quiz
Module 1 Quiz
Meet Your Fellow Global Classmates
1.2 Discussion Prompt
End of Module 1 Discussion Prompt
Introducing Logistic Regression
Limitations of the 2x2 Table Analysis
Logistic Regression: A First Look
Visualizing and Interpreting a Logistic Regression
Revising the 2x2 Example: WCGS Cardiovascular Study
Guided practice: Fitting a Simple Logistic Regression Against One Variable
Extending the WCGS Cardiovascular Model with Multivariable Logistic Regression
Prediction with Multivariable Logistic Regression
Logistic Regression: A Recap and Review
Guided Practice: Fitting a Logistic Regression Against More Than One Variable
Guided Practice: Calculating Predicted Probabilities
Guided Practice: Visualizing a Fitted Logistic Regression Model
2.1 Independent Guide
2.2 Independent Guide
2.1 Practice Quiz
2.2 Practice Quiz
Module 2 Quiz
Assessing the Predictive Accuracy of Logistic Regression Models
Why Do We Need to Assess Predictions?
Extracting Probabilities from a Logistic Regression
How Do We Determine if Predicted Probabilities are "Good"?
Model Calibration
Hosmer-Lemeshow Test
Model Discrimination
Changing the Cutpoint Changes Sensitivity and Specificity
Receiver Operating Characteristic (ROC) Curve
Area Under the ROC Curve (AUC)
AUC Example: Risk of Coronary Heart Disease
Brier Score
Cross Validation
Guided Practice:
Assessing the Predictive Ability of Logistic Regression Models
Guided Practice: ROC and AUC
Guided Practice: Brier Score
Case Study: Treatment of Testicular Cancer
3.3 Independent Guide
End of Module 3 Discussion Prompt Suggested Answer
Post-Course Survey
3.3 Practice Quiz
Module 3 Quiz
End of Module 3 Discussion Prompt
Logistic Regression and Prediction for Health Data at Coursera Admission Process
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