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Modern Statistical Computing and Regression Modeling in R · LearnSpace
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Modern Statistical Computing and Regression Modeling in R

Курс от Arizona State University
Средний≈ 11.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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. You will learn how R, an interpreted language for analyzing and visualizing data, can be used to accomplish regression analysis, and will have an opportunity to practice with given data sets and code.

Навыки, которые вы освоите

Data StorageData TransformationMathematical SoftwareModel EvaluationData StoreDatabase SoftwareProbability & StatisticsData ManipulationData Storage TechnologiesData AccessStatistics

Программа курса

4 модулей · 68 учебных материалов

01Course Introduction19 материалов

Introduction and Resources

Course IntroductionВидеоCourse Resources and Peer ReviewsЧтениеCourse GitHub Repository - For Practice with Data Sets and CodeЧтение

Instructor Bios

Instructor BiosЧтение

Tools and Technology for Statisticians and Data Scientists

Учитесь у экспертов

Anthony Kuhn

Director of Design Experiences

Edgar Hassler

Principal Data Scientist

Modern Statistical Computing and Regression Modeling in R
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Обучение на Coursera

≈ 11.1 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Section OverviewЧтение

Working with Data in Computing Environments

A Century of Statistical ComputingВидеоA Century of Statistical ComputingЧтение

Introduction to Excel

Excel for Data AnalysisВидео

Introduction to Tableau

Basic Operations in TableauВидео

Introduction to R and RStudio

Getting Started with R and RStudioЧтениеIntroduction to R and RStudio, Part 1ВидеоIntroduction to R and RStudio, Part 2ВидеоDemos in R - Resources for Navigating the R EnvironmentЧтение

Conveying Results with R Markdown

R Markdown DemoВидеоRMarkdown Alternative: QuartoЧтение

Jupyter Notebooks, Kernels, and Databricks

Reading for Installation of JupyterЧтениеJupyter Notebooks, Kernels, and DatabricksВидеоJupyter Lab DemoВидеоPractice quiz for Tools and Technology for Statisticians and Data ScientistsЗадание
02Using R for Simulation14 материалов

Monte Carlo Simulations and Random Number Generation

Monte Carlo SimulationsВидеоDistributions and PRNG in RВидео

Parallel Computing

Parallel Computing in R Lecture - Video Segment OverviewЧтениеSegment 1: Introduction to Parallel Computing: Benefits and ApplicationsВидеоSegment 2: Parallel Computation in R: Parallel Library and Worker Types ВидеоSegment 3: Solving for Side Effects and Optimizing Parallel Computing ВидеоSegment 4: Worker Cluster Set-Up and Demo ВидеоUsing a Simple Cluster DemoВидео

R's PWR Package and Monte Carlo Simulations

Simulation Study in R Lecture - Video Chapter OverviewЧтениеSegment 1: Introduction and Testing a Website Change ВидеоSegment 2: Perform the Test ВидеоSegment 3: Long Run Performance & Unplanned Early Stopping ВидеоSegment 4: Changing Success Rate ВидеоPractice Quiz for Using R SimulationЗадание
03Linear Model Regression, Diagnostics, and Penalized Versions26 материалов

Linear Regression and Diagnostics in R

Chapter 11: Simple Linear Regression and Correlation (Optional)ЧтениеOrdinary Linear RegressionВидеоDiagnostics & Remediation Lecture - Video Segment OverviewЧтениеSegment 1: Introduction to Diagnostics and RemediationВидеоSegment 2: Introduction to Anscombe’s Quartet and Diagnostic PlotsВидеоSegment 3: Influence Diagnostics and PlotsВидеоSegment 4: Solving for the Problem of MulticollinearityВидеоSegment 5: Solving for the Problem of Non-Constant VarianceВидео

Computing in R for Complications to Standard Regression

Linear Model and ScopeВидеоFormula and Factors, Part 1ВидеоModel Matrix and Wilkinson NotationВидеоFormula and Factors, Part 2 Lecture - Video Segment OverviewЧтениеSegment 1: Scaling Numeric FactorsВидеоSegment 2: Handling Categorical FactorsВидео

Regularization in R

Regularization Lecture - Video Segment OverviewЧтениеSegment 1: The Need for Regularized RegressionВидеоSegment 2: Introduction to Regularization Methods and ToolsВидеоSegment 3: Comparative Example: Ridge Regression Versus Lasso RegressionВидеоSegment 4: Cross-Validation Simulation Example: Ridge RegressionВидеоSegment 5: Cross-Validation Example: Lasso RegressionВидео
04Nonlinear Regression in R9 материалов

Linearization

Using the Linear Model with TransformationsВидеоNonlinear Regression in RЗадание

Generalized Linear Models in R

Generalized Linear Models in R - Video Segment OverviewЧтениеSegment 1: Introduction to GLM Implementation in RВидеоSegment 2: Pneumoconiosis Data Analysis Example with GLMВидеоSegment 3: Aircraft Damage Data Analysis ExampleВидеоSegment 4: Worsted Yarn Data Re-Visited, Summary and Further Considerations for GLMsВидеоNonlinear Regression QuizЗаданиеMini-Project for Modern Statistics for Data-Driven Decision-MakingВзаимная проверка
Segment 3: Define a Factor in RВидео
Segment 4: Web Site Test and Dummy CodingВидео
Segment 5: Effect CodingВидео
Segment 6: Setting Coding in RВидео
Segment 7: Factors and FittingВидео
Practice Quiz for Linear Model Regression, Diagnostics, and Penalized VersionsЗадание