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Modern Regression Analysis in R · LearnSpace
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Modern Regression Analysis in R

Курс от University of Colorado Boulder
Средний≈ 45.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash

Навыки, которые вы освоите

Regression AnalysisStatistical ModelingModel EvaluationR ProgrammingStatistical InferencePredictive ModelingStatistical AnalysisStatistical Hypothesis TestingStatistical MethodsData EthicsData ScienceCorrelation AnalysisPredictive AnalyticsProbability & StatisticsStatistical ProgrammingPlot (Graphics)

Программа курса

6 модулей · 84 учебных материалов

01Introduction to Statistical Models19 материалов

Optional Introduction to R and Jupyter

Optional Introduction to Jupyter and RПрограммирование

Introduction to Statistical Modeling

Course Updates and Accessibility SupportЧтениеEarn Academic Credit for your Work!ЧтениеCourse SupportЧтение

Учитесь у экспертов

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Modern Regression Analysis in R
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 45.5 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Часть программы вашего университета
Assessment ExpectationsЧтение
Introduce YourselfОбсуждение
Frameworks and Goals of Statistical ModelingВидео
The Assumption of Concept ValidityВидео
Introduction to Statistical ModelingЗадание

The Linear Regression Model

The Linear Regression ModelВидеоMatrix Representation of the Linear Regression ModelВидеоAssumptions of Linear RegressionВидеоThe Appropriateness of Linear RegressionВидеоInterpreting the Linear Regression Model IВидеоInterpreting the Linear Regression Model IIВидеоThe Linear Regression ModelЗадание

Assignments: Linear Regression and Standardization

Module 1: Peer Reviewed LabЛабораторнаяModule 1 Peer Review SubmissionВзаимная проверкаModule 1 AutogradedПрограммирование
02Linear Regression Parameter Estimation14 материалов

Least Squares

Introduction to Least SquaresВидеоLinear Algebra for Least SquaresВидеоDeriving the Least Squares SolutionВидеоRegression Modeling in R: a First PassВидеоJustifying Least Squares: the Gauss-Markov Theorem and Maximum Likelihood EstimationВидеоLeast SquaresЗадание

Variability and Identifiability in Regression Models

Sums of Squares and Estimating the Error VarianceВидеоThe Coefficient of DeterminationВидеоThe Problem of Non-identifiabiliityВидеоRegression Modeling in R: a Second PassВидеоVariability and Identifiability in Regression ModelsЗадание

Assignments: Least Squares, Parameter Estimation and Interpreting SLR Models

Module 2 Peer Reviewed LabЛабораторнаяModule 2 Peer Review SubmissionВзаимная проверкаModule 2 Autograded AssignmentПрограммирование
03Inference in Linear Regression15 материалов

Statistical Inference: Introduction and t-tests

Motivating Statistical Inference in the Linear Regression ContextВидеоThe Sampling Distribution of the Least Squares EstimatorВидеоT-Tests for Individual Regression ParametersВидеоT-Tests in RВидеоStatistical Inference: Intro and T-TestsЗадание

Statistical Inference: the F-tests and Confidence Intervals

Motivating the F-Test: Multiple Statistical ComparisonsВидеоThe F-TestВидеоThe F-Test in RВидеоConfidence Intervals in the Regression ContextConfidence Intervals in the Regression ContextВидеоStatistical Inference: the F-tests and Confidence IntervalsЗадание

Ethics in Statistical Practice

Ethics in Statistical Practice and Communication: Five RecommendationsЧтениеEthics in Statistical Practice and Communication: Five RecommendationsВзаимная проверка

Assignments: Coefficient Importance and Confidence Intervals

Module 3 Peer Reviewed LabЛабораторнаяModule 3 Peer Review SubmissionВзаимная проверкаModule 3 Autograded AssignmentПрограммирование
04Prediction and Explanation in Linear Regression Analysis10 материалов

Prediction

Differentiating Prediction and ExplanationВидеоPoint Estimates for PredictionВидеоInterval Estimates for PredictionВидеоMaking Predictions Using Real Data in RВидеоWhen Prediction Goes WrongВидеоPredictionЗадание

Explanation

Defining CausalityВидео

Assignments: Predictive Models, Prediction Intervals and Experimental Design

Module 4 Peer Review LabЛабораторнаяModule 4 Peer Review SubmissionВзаимная проверкаModule 4 Autograded AssignmentПрограммирование
05Regression Diagnostics11 материалов

Diagnostics I: Linearity and Independence

Linear Regression Diagnostic MethodsВидеоViolations of the Linearity AssumptionВидеоViolations of the Independence AssumptionВидеоDiagnostics I: Linearity and IndependenceЗадание

Diagnostics II: Constant Variance and Normality

Violations of the Constant Variance AssumptionВидеоViolations of the Normality AssumptionВидеоDiagnostics in RВидеоDiagnostics II: Constant Variance and NormalityЗадание

Assignments: Regression Assumptions and Diagnostics

Module 5 Peer Review AssignmentЛабораторнаяModule 5 Peer Review SubmissionВзаимная проверкаModule 5 Autograded AssignmentПрограммирование
06Model Selection and Multicollinearity15 материалов

Model Selection I: Testing-based Procedures

Motivating Model Selection MethodsВидеоTesting-Based Procedures and their ShortfallsВидео

Model Selection II: Criterion-based Procedures

Criterion-Based Procedures: AICВидеоCriterion-Based Procedures: BICВидеоCriterion-Based Procedures: Adjusted R-SquaredВидеоThe Mean Squared Prediction Error as a Model Selection Method ВидеоModel Selection in RВидеоModel Selection II: Criterion-based ProceduresЗадание

Multicollinearity

The Problem of CollinearityВидеоDiagnosing Multicollinearity ВидеоThe Problem of Multicollinearity: Solutions and R Implementation ВидеоMulticollinearityЗадание

Assignments: Model Selection, Information Criterion and Multicollinearity

Module 6 Peer Review LabЛабораторнаяModule 6 Peer Review SubmissionВзаимная проверкаModule 6 Autograded AssignmentПрограммирование