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Modern Statistical Computing and Regression Modeling in R

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Modern Statistical Computing and Regression Modeling in R

Dieser Kurs ist Teil von Spezialisierung „Modern Statistics for Data-Driven Decision-Making“

Dozenten: Anthony Kuhn

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Learners will understand computer applications for working with data, and concepts & applications of using R for regression analysis.

Kompetenzen, die Sie erwerben

Kategorie: Probability & Statistics
Kategorie: Data Store
Kategorie: Data Storage
Kategorie: Statistical Machine Learning
Kategorie: Statistics
Kategorie: Data Access
Kategorie: Model Evaluation
Kategorie: Data Manipulation
Kategorie: Data Storage Technologies
Kategorie: Data Transformation
Kategorie: Statistical Hypothesis Testing

Werkzeuge, die Sie lernen werden

Kategorie: Mathematical Software
Kategorie: Database Software

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

Januar 2026

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5 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 4 Module

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.

Moduldetails

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.

Das ist alles enthalten

9 Videos9 Lektüren1 Aufgabe

9 VideosInsgesamt 59 Minuten

Course Introduction3 Minuten
A Century of Statistical Computing8 Minuten
Excel for Data Analysis16 Minuten
Basic Operations in Tableau5 Minuten
Introduction to R and RStudio, Part 16 Minuten
Introduction to R and RStudio, Part 210 Minuten
R Markdown Demo4 Minuten
Jupyter Notebooks, Kernels, and Databricks3 Minuten
Jupyter Lab Demo3 Minuten

9 LektürenInsgesamt 113 Minuten

Course Resources and Peer Reviews5 Minuten
Course GitHub Repository - For Practice with Data Sets and Code10 Minuten
Instructor Bios10 Minuten
Section Overview3 Minuten
A Century of Statistical Computing5 Minuten
Getting Started with R and RStudio10 Minuten
Demos in R - Resources for Navigating the R Environment30 Minuten
RMarkdown Alternative: Quarto10 Minuten
Reading for Installation of Jupyter30 Minuten

1 AufgabeInsgesamt 30 Minuten

Practice quiz for Tools and Technology for Statisticians and Data Scientists30 Minuten

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.

Das ist alles enthalten

11 Videos2 Lektüren1 Aufgabe

11 VideosInsgesamt 56 Minuten

Monte Carlo Simulations9 Minuten
Distributions and PRNG in R11 Minuten
Segment 1: Introduction to Parallel Computing: Benefits and Applications4 Minuten
Segment 2: Parallel Computation in R: Parallel Library and Worker Types 3 Minuten
Segment 3: Solving for Side Effects and Optimizing Parallel Computing 2 Minuten
Segment 4: Worker Cluster Set-Up and Demo 5 Minuten
Using a Simple Cluster Demo6 Minuten
Segment 1: Introduction and Testing a Website Change 3 Minuten
Segment 2: Perform the Test 4 Minuten
Segment 3: Long Run Performance & Unplanned Early Stopping 5 Minuten
Segment 4: Changing Success Rate 4 Minuten

2 LektürenInsgesamt 20 Minuten

Parallel Computing in R Lecture - Video Segment Overview10 Minuten
Simulation Study in R Lecture - Video Chapter Overview10 Minuten

1 AufgabeInsgesamt 30 Minuten

Practice Quiz for Using R Simulation30 Minuten

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.

Das ist alles enthalten

21 Videos4 Lektüren1 Aufgabe

21 VideosInsgesamt 70 Minuten

Ordinary Linear Regression7 Minuten
Segment 1: Introduction to Diagnostics and Remediation1 Minute
Segment 2: Introduction to Anscombe’s Quartet and Diagnostic Plots4 Minuten
Segment 3: Influence Diagnostics and Plots6 Minuten
Segment 4: Solving for the Problem of Multicollinearity2 Minuten
Segment 5: Solving for the Problem of Non-Constant Variance2 Minuten
Linear Model and Scope2 Minuten
Formula and Factors, Part 110 Minuten
Model Matrix and Wilkinson Notation5 Minuten
Segment 1: Scaling Numeric Factors2 Minuten
Segment 2: Handling Categorical Factors1 Minute
Segment 3: Define a Factor in R2 Minuten
Segment 4: Web Site Test and Dummy Coding3 Minuten
Segment 5: Effect Coding3 Minuten
Segment 6: Setting Coding in R2 Minuten
Segment 7: Factors and Fitting1 Minute
Segment 1: The Need for Regularized Regression5 Minuten
Segment 2: Introduction to Regularization Methods and Tools4 Minuten
Segment 3: Comparative Example: Ridge Regression Versus Lasso Regression2 Minuten
Segment 4: Cross-Validation Simulation Example: Ridge Regression3 Minuten
Segment 5: Cross-Validation Example: Lasso Regression3 Minuten

4 LektürenInsgesamt 100 Minuten

Chapter 11: Simple Linear Regression and Correlation (Optional)70 Minuten
Diagnostics & Remediation Lecture - Video Segment Overview10 Minuten
Formula and Factors, Part 2 Lecture - Video Segment Overview10 Minuten
Regularization Lecture - Video Segment Overview10 Minuten

1 AufgabeInsgesamt 30 Minuten

Practice Quiz for Linear Model Regression, Diagnostics, and Penalized Versions30 Minuten

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.

Das ist alles enthalten

5 Videos1 Lektüre2 Aufgaben1 peer review

5 VideosInsgesamt 29 Minuten

Using the Linear Model with Transformations13 Minuten
Segment 1: Introduction to GLM Implementation in R4 Minuten
Segment 2: Pneumoconiosis Data Analysis Example with GLM6 Minuten
Segment 3: Aircraft Damage Data Analysis Example4 Minuten
Segment 4: Worsted Yarn Data Re-Visited, Summary and Further Considerations for GLMs2 Minuten