ANOVA and Experimental Design
Master statistical analysis techniques through ANOVA, ANCOVA, and experimental design for data science applications.
Master statistical analysis techniques through ANOVA, ANCOVA, and experimental design for data science applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistical Modeling for Data Science Applications Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Master ANOVA and ANCOVA as regression models
Implement two-way ANOVA for complex analyses
Design experiments using randomization and blocking
Conduct hypothesis testing in ANOVA context
Apply factorial design principles
Skills you'll gain
This course includes:
5.6 Hours PreRecorded video
24 quizzes, 4 programming assignments, 4 peer reviews
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design principles. Students learn to implement these statistical techniques as linear regression models for data science applications. The curriculum includes advanced topics such as randomization, blocking, factorial design, and causality analysis. Through hands-on programming assignments and real-world examples, participants develop practical skills in designing and analyzing experiments while considering ethical implications.
Introduction to ANOVA and Experimental Design
Module 1 · 11 Hours to complete
Hypothesis Testing in the ANOVA Context
Module 2 · 8 Hours to complete
Two-Way ANOVA and Interactions
Module 3 · 9 Hours to complete
Experimental Design: Basic Concepts and Designs
Module 4 · 10 Hours to complete
Fee Structure
Instructor
Director of Applied Mathematics Program and Statistical Modeling Educator
Brian Zaharatos is the Director of the Professional Master’s Degree in Applied Mathematics at the University of Colorado Boulder. He teaches several courses, including ANOVA and Experimental Design, Generalized Linear Models and Nonparametric Regression, and Modern Regression Analysis in R. His courses focus on statistical modeling techniques essential for data analysis and research applications.
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