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Modern Statistical Computing and Regression Modeling in R

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Modern Statistical Computing and Regression Modeling in R

Ce cours fait partie de Spécialisation "Modern Statistics for Data-Driven Decision-Making"

Instructeurs : Anthony Kuhn

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Outils que vous découvrirez

Catégorie : Mathematical Software
Catégorie : Database Software

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

janvier 2026

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5 devoirs

Enseigné en Anglais

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Ce cours fait partie de la Spécialisation "Modern Statistics for Data-Driven Decision-Making"

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Il y a 4 modules dans ce cours

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.

Détails du module

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.

Inclus

9 vidéos9 lectures1 devoir

9 vidéosTotal 59 minutes

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

9 lecturesTotal 113 minutes

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

1 devoirTotal 30 minutes

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

Inclus

11 vidéos2 lectures1 devoir

11 vidéosTotal 56 minutes

Monte Carlo Simulations9 minutes
Distributions and PRNG in R11 minutes
Segment 1: Introduction to Parallel Computing: Benefits and Applications4 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 Demo6 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 lecturesTotal 20 minutes

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

1 devoirTotal 30 minutes

Practice Quiz for Using R Simulation30 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.

Inclus

21 vidéos4 lectures1 devoir

21 vidéosTotal 70 minutes

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

4 lecturesTotal 100 minutes

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

1 devoirTotal 30 minutes

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

Inclus

5 vidéos1 lecture2 devoirs1 évaluation par les pairs

5 vidéosTotal 29 minutes

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

1 lectureTotal 10 minutes

Generalized Linear Models in R - Video Segment Overview10 minutes

2 devoirsTotal 60 minutes

Nonlinear Regression in R30 minutes
Nonlinear Regression Quiz30 minutes

1 évaluation par les pairsTotal 60 minutes

Mini-Project for Modern Statistics for Data-Driven Decision-Making60 minutes

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