- Specialization: Statistical Modeling for Data Science Applications
- Instructor: Dr. Brian Zaharatos, Senior Instructor of Applied Mathematics, Director of the Professional Master’s Degree in Applied Mathematics
- Prior knowledge needed: Basic calculus (differentiation and integration), linear algebra, probability theory, basic statistical inference
Successful completion of this course demonstrates your achievement of the following learning outcomes for the MS-DS on Coursera:
In this module, we will introduce the basic conceptual framework for experimental design and define the models that will allow us to answer meaningful questions about the differences between group means with respect to a continuous variable. Such models include the one-way Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models.
In this module, we will learn how statistical hypothesis testing and confidence intervals, in the ANOVA/ANCOVA context, can help answer meaningful questions about the differences between group means with respect to a continuous variable.
In this module, we will study the two-way ANOVA model and use it to answer research questions using real data.
In this module, we will study fundamental experimental design concepts, such as randomization, treatment design, replication, and blocking. We will also look at basic factorial designs as an improvement over elementary “one factor at a time” methods. We will combine these concepts with the ANOVA and ANCOVA models to conduct meaningful experiments.
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