What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Provides a solid grounding in the theoretical concepts behind logistic regression.
- Covers the essentials of regression analysis and explains its role in understanding relationships between variables.
- Introduces different techniques used to estimate probabilities within regression models.
- Breaks down the core ideas and principles that define logistic regression.
- Explains why logistic regression is preferred over ordinary least squares (OLS) when working with binary outcomes.
- Demonstrates how logistic regression models are built and executed using SAS software.
- Outlines methods for evaluating model accuracy, assessing goodness-of-fit, and reviewing overall performance.
- Teaches practical ways to interpret logistic regression outputs and apply insights to decision-making.
- Highlights approaches for fine-tuning and improving models using SAS-based optimization techniques.
- Applies concepts to real-world datasets to showcase predictive modeling and risk assessment in action.
- Guides learners in developing logistic regression models within SAS.
- Offers an in-depth understanding of regression analysis as a whole.
- Clarifies the purpose and advantages of logistic regression.
- Details the essential components that make up a logistic regression model.
- Explains multiple methods used to compute probabilities.
- Guides interpreting model results and communicating findings effectively.
- Helps learners understand SAS-generated logistic regression outputs with clarity and precision.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression | 1h 58m | ✔ | View Curriculum |
Description
This opening module offers participants a clear overview of the course goals and highlights the role of logistic regression in predictive analytics and risk evaluation.
Regression Analysis
Learners explore the core principles of regression analysis and its value in identifying and quantifying relationships between variables. A structured set of lessons introduces essential concepts, methodologies, and practical applications.
Predicting Probabilities
This segment explains multiple techniques for estimating probabilities in regression models. Through examples and guided discussions, participants gain clarity on the methods and how they are applied in real-world analytical tasks.
Logistic Regression
Participants are introduced to the foundations of logistic regression, examining its purpose, structure, and theoretical basis. The section also explains why logistic regression is more suitable than ordinary least squares (OLS) for modeling binary outcomes, offering insight into how the model is constructed.
SAS Methodology
This module presents hands-on instruction for building and analyzing logistic regression models in SAS. Step-by-step demonstrations guide participants through implementation, model evaluation, and interpretation of SAS-generated outputs.
Model Fit and Course Summary
The final section focuses on methods for assessing model performance and understanding goodness-of-fit measures. It concludes with a comprehensive recap of the concepts and techniques covered throughout the course.
By the end of the program, participants gain a thorough understanding of logistic regression, key analytical techniques, and their execution within SAS. They complete the course prepared to apply logistic regression confidently across multiple domains, enabling informed, data-driven decision-making.
Requirements
- Participants are expected to have a basic understanding of SAS before enrolling.
- No prior technical expertise is necessary, as the course is designed to support learners at all levels.
- A general interest in data analysis or predictive modeling will help participants engage more effectively with the content.
- Familiarity with statistical concepts is beneficial but not mandatory.
Target Audience
- Ideal for researchers, forensic statisticians, data mining professionals, environmental scientists, and epidemiologists.
- Suited for anyone interested in building predictive models and estimating the likelihood of specific outcomes.
