Welcome to Modern Statistical Computing and Regression Modeling in R. In this course, you will become familiar with computer applications for working with data, including Excel, R, Tableau, and Jupyter Notebooks; and will learn concepts and applications of Monte Carlo methods and regression analysis.
Modern Statistical Computing and Regression Modeling in R
Modern Statistical Computing and Regression Modeling in R
This course is part of Modern Statistics for Data-Driven Decision-Making Specialization
Instructors: Anthony Kuhn
Access provided by Xavier School of Management, XLRI
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learners will understand computer applications for working with data, and concepts & applications of using R for regression analysis.
Skills you'll gain
Details to know
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Assessments
5 assignments
Taught in English
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January 2026
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This course is part of the Modern Statistics for Data-Driven Decision-Making Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. In this course, learners will develop facility for using software applications for data storage, analysis, and presentation; and will be able to employ Monte Carlo simulations and regression models in working with data. Learn more about the instructors who developed this course. Read the instructor bios and review the learning outcomes for the course.
What's included
9 videos9 readings1 assignment
In this module, we will explore pseudo random number generators, learn about seeds and use a seed to generate reproducible results. We will use R’s d, p, q, and r functions to measure and generate random variates. We will conduct a Monte Carlo simulation of an experiment and analyze results from the hypothesis tests executed in R using simulated data.
What's included
11 videos2 readings1 assignment
In this module, we re-visit the ordinary linear regression model. We also use R to fit a regression model and display and interpret model-fit statistics and coefficient summaries and tests.
What's included
21 videos4 readings1 assignment
In this module, you will use data sets to review and calculate linear and nonlinear models. Be sure to view videos for this module, complete the readings, and any assignments. Begin by reviewing the learning objectives before beginning work in this module.
What's included
5 videos1 reading2 assignments1 peer review
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