STAT 300: Regression Analysis
Master linear and multiple regression models for predictive modeling and data analysis.
16h
Duration
22
Lessons
4.9/5
Rating
150+
Students
Course Preview
Course Description
This course focuses on linear and multiple regression models, teaching students how to build, interpret, and validate regression models. Topics include model assumptions, residual analysis, and applications to predictive modeling in various disciplines. Essential for data scientists, researchers, and analysts who need to understand relationships between variables.
What You'll Learn
Fundamental statistical concepts and principles
Data visualization and interpretation techniques
Hypothesis testing and statistical inference
Real-world application of statistical methods
Statistical software tools and techniques
Confidence intervals and margin of error
Course Content
Module 1: Linear Regression
- Simple Linear Regression (40 min)
- Multiple Regression (45 min)
- Model Assumptions (35 min)
- Residual Analysis (30 min)
Module 2: Advanced Regression
- Model Selection (40 min)
- Multicollinearity (25 min)
- Nonlinear Regression (35 min)
- Predictive Modeling (45 min)
Requirements
Prerequisites
- Basic mathematics knowledge (high school level)
- Computer with internet access
- Willingness to practice with real datasets
- No prior statistics experience required
$300
One-time payment • Lifetime access
This course includes:
8+ hours of video content
Downloadable resources
Lifetime access
Mobile friendly
Certificate of completion
Expert support
Your Instructor
Statistics Expert
Professional Statistics Educator with 5+ years of experience teaching students and professionals.
500+ Students Taught
4.9/5 Average Rating
15+ Courses Created
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