This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
Modern Regression Analysis in R
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Modern Regression Analysis in R
This course is part of Statistical Modeling for Data Science Applications Specialization
Instructor: Brian Zaharatos
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Gain insight into a topic and learn the fundamentals.
4.4
(29 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 45 hours
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What you'll learn
Articulate some recommended practices for ethical behavior and communication in statistics and data science.
Interpret important components of the MLR model, including the “systematic” and “random” components of the model.
Describe and implement testing-based procedures for model selections and select a “best” model based on a given procedure.
Skills you'll gain
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Assessments
11 quizzes
Taught in English
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This course is part of the Statistical Modeling for Data Science Applications Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
What's included
8 videos3 readings2 quizzes2 programming assignments1 peer review1 discussion prompt1 ungraded lab
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.
What's included
9 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
What's included
8 videos1 reading2 quizzes1 programming assignment2 peer reviews1 ungraded lab
In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
What's included
6 videos1 quiz1 programming assignment1 peer review1 ungraded lab
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.
What's included
6 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
In this module, we will study methods for model selection and model improvement.. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods, and will learn about the problem of multicollinearity (also called collinearity).
What's included
10 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
Instructor
Instructor ratings
4.7 (11 ratings)Offered by
Recommended if you're interested in Probability and Statistics
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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