- Specialization: Statistical Modeling for Data Science Applications
- Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics
- Prior knowledge needed: Basic calculus (differentiation and integration), linear algebra, probability theory
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 statistical modeling in general, and linear statistical models in particular.
In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
In this module, we will identify how models can predict future values, as well as construct confidence intervals for those values. We will also explore the relationship between statistical modelling and causal explanations.
In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is appropriate for the data at hand.
In this module, we will identify measures to improve our models after they have been fit to the data. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods and multicollinearity diagnostics.
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