University of Colorado Boulder - Modern Regression Analysis in R
- Offered byCoursera
Modern Regression Analysis in R at Coursera Overview
Duration | 45 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Modern Regression Analysis in R at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 3 in the Statistical Modeling for Data Science Applications Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Calculus, linear algebra, and probability theory.
- Approx. 45 hours to complete
- English Subtitles: English
Modern Regression Analysis in R at Coursera Course details
- 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.
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Modern Regression Analysis in R at Coursera Curriculum
Introduction to Statistical Models
Frameworks and Goals of Statistical Modeling
The Assumption of Concept Validity
The Linear Regression Model
Matrix Representation of the Linear Regression Model
Assumptions of Linear Regression
The Appropriateness of Linear Regression
Interpreting the Multiple Linear Regression Model I
Interpreting the Multiple Linear Regression Model II
Introduction to Statistical Modeling
The Linear Regression Model
Linear Regression Parameter Estimation
Introduction to Least Squares
Linear Algebra for Least Squares
Deriving the Least Squares Solution
Regression Modeling in R: a First Pass
Justifying Least Squares: the Gauss-Markov Theorem and Maximum Likelihood Estimation
Sums of Squares and Estimating the Error Variance
The Coefficient of Determination
The Problem of Non-identifiabiliity
Regression Modeling in R: a Second Pass
Least Squares
Variability and Identifiability in Regression Models
Inference in Linear Regression
Motivating Statistical Inference in the Linear Regression Context
The Sampling Distribution of the Least Squares Estimator
T-Tests for Individual Regression Parameters
T-Tests in R
Motivating the F-Test: Multiple Statistical Comparisons
The F-Test
The F-Test in R
Confidence Intervals in the Regression ContextConfidence Intervals in the Regression Context
Ethics in Statistical Practice and Communication: Five Recommendations
Statistical Inference: Intro and T-Tests
Statistical Inference: the F-tests and Confidence Intervals
Prediction and Explanation in Linear Regression Analysis
Differentiating Prediction and Explanation
Point Estimates for Prediction
Interval Estimates for Prediction
Making Predictions Using Real Data in R
When Prediction Goes Wrong
Defining Causality
Prediction
Regression Diagnostics
Linear Regression Diagnostic Methods
Violations of the Linearity Assumption
Violations of the Independence Assumption
Violations of the Constant Variance Assumption
Violations of the Normality Assumption
Diagnostics in R
Diagnostics I: Linearity and Independence
Diagnostics II: Constant Variance and Normality
Model Selection and Multicollinearity
Motivating Model Selection Methods
Testing-Based Procedures and their Shortfalls
Criterion-Based Procedures: AIC
Criterion-Based Procedures: BIC
Criterion-Based Procedures: Adjusted R-Squared
The Mean Squared Prediction Error as a Model Selection Method
Model Selection in R
The Problem of Collinearity
Diagnosing Multicollinearity
The Problem of Multicollinearity: Solutions and R Implementation
Model Selection II: Criterion-based Procedures
Multicollinearity
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