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

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4 modules

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

4 modules

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

Tools you'll learn

Database Software

Details to know

Shareable certificate

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Assessments

5 assignments

Taught in English

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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

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.

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

9 videos Total 59 minutes

Course Introduction 3 minutes
A Century of Statistical Computing 8 minutes
Excel for Data Analysis 16 minutes
Basic Operations in Tableau 5 minutes
Introduction to R and RStudio, Part 1 6 minutes
Introduction to R and RStudio, Part 2 10 minutes
R Markdown Demo 4 minutes
Jupyter Notebooks, Kernels, and Databricks 3 minutes
Jupyter Lab Demo 3 minutes

9 readings Total 113 minutes

Course Resources and Peer Reviews 5 minutes
Course GitHub Repository - For Practice with Data Sets and Code 10 minutes
Instructor Bios 10 minutes
Section Overview 3 minutes
A Century of Statistical Computing 5 minutes
Getting Started with R and RStudio 10 minutes
Demos in R - Resources for Navigating the R Environment 30 minutes
RMarkdown Alternative: Quarto 10 minutes
Reading for Installation of Jupyter 30 minutes

1 assignment Total 30 minutes

Practice quiz for Tools and Technology for Statisticians and Data Scientists 30 minutes

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

11 videos Total 56 minutes

Monte Carlo Simulations 9 minutes
Distributions and PRNG in R 11 minutes
Segment 1: Introduction to Parallel Computing: Benefits and Applications 4 minutes
Segment 2: Parallel Computation in R: Parallel Library and Worker Types 3 minutes
Segment 3: Solving for Side Effects and Optimizing Parallel Computing 2 minutes
Segment 4: Worker Cluster Set-Up and Demo 5 minutes
Using a Simple Cluster Demo 6 minutes
Segment 1: Introduction and Testing a Website Change 3 minutes
Segment 2: Perform the Test 4 minutes
Segment 3: Long Run Performance & Unplanned Early Stopping 5 minutes
Segment 4: Changing Success Rate 4 minutes

2 readings Total 20 minutes

Parallel Computing in R Lecture - Video Segment Overview 10 minutes
Simulation Study in R Lecture - Video Chapter Overview 10 minutes

1 assignment Total 30 minutes

Practice Quiz for Using R Simulation 30 minutes

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

21 videos Total 70 minutes

Ordinary Linear Regression 7 minutes
Segment 1: Introduction to Diagnostics and Remediation 1 minute
Segment 2: Introduction to Anscombe’s Quartet and Diagnostic Plots 4 minutes
Segment 3: Influence Diagnostics and Plots 6 minutes
Segment 4: Solving for the Problem of Multicollinearity 2 minutes
Segment 5: Solving for the Problem of Non-Constant Variance 2 minutes
Linear Model and Scope 2 minutes
Formula and Factors, Part 1 10 minutes
Model Matrix and Wilkinson Notation 5 minutes
Segment 1: Scaling Numeric Factors 2 minutes
Segment 2: Handling Categorical Factors 1 minute
Segment 3: Define a Factor in R 2 minutes
Segment 4: Web Site Test and Dummy Coding 3 minutes
Segment 5: Effect Coding 3 minutes
Segment 6: Setting Coding in R 2 minutes
Segment 7: Factors and Fitting 1 minute
Segment 1: The Need for Regularized Regression 5 minutes
Segment 2: Introduction to Regularization Methods and Tools 4 minutes
Segment 3: Comparative Example: Ridge Regression Versus Lasso Regression 2 minutes
Segment 4: Cross-Validation Simulation Example: Ridge Regression 3 minutes
Segment 5: Cross-Validation Example: Lasso Regression 3 minutes

4 readings Total 100 minutes

Chapter 11: Simple Linear Regression and Correlation (Optional) 70 minutes
Diagnostics & Remediation Lecture - Video Segment Overview 10 minutes
Formula and Factors, Part 2 Lecture - Video Segment Overview 10 minutes
Regularization Lecture - Video Segment Overview 10 minutes

1 assignment Total 30 minutes

Practice Quiz for Linear Model Regression, Diagnostics, and Penalized Versions 30 minutes

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

5 videos Total 29 minutes

Using the Linear Model with Transformations 13 minutes
Segment 1: Introduction to GLM Implementation in R 4 minutes
Segment 2: Pneumoconiosis Data Analysis Example with GLM 6 minutes
Segment 3: Aircraft Damage Data Analysis Example 4 minutes
Segment 4: Worsted Yarn Data Re-Visited, Summary and Further Considerations for GLMs 2 minutes