Statistics with R: Data Analysis and Inference
Master statistical concepts and R programming for data analysis in this comprehensive review course.
Master statistical concepts and R programming for data analysis in this comprehensive review course.
This advanced statistics course provides a thorough review of descriptive and inferential statistics using the R programming language. Students learn to analyze real-world data through visualization, hypothesis testing, and regression analysis. The curriculum covers essential statistical concepts and their practical application using R, making it ideal for those preparing for graduate studies or professional work in data science and engineering fields.
Instructors:
English
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What you'll learn
Create and interpret numerical and graphical data summaries
Develop and evaluate parameter estimators using various techniques
Conduct hypothesis tests and construct confidence intervals
Perform linear regression analysis with multiple variables
Apply bootstrapping and resampling methods
Implement statistical analyses using R software
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This comprehensive statistics course combines theoretical understanding with practical application using R software. Students learn both descriptive and inferential statistics, including data visualization, parameter estimation, hypothesis testing, and regression analysis. The curriculum emphasizes hands-on practice with real datasets using R, while covering advanced topics like bootstrapping and resampling methods. Through the Grasple platform, students receive immediate feedback on exercises, ensuring thorough understanding of statistical concepts.
Descriptive Statistics
Module 1 · 0 Weeks to complete
Estimator Theory
Module 2 · 0 Weeks to complete
Hypothesis Testing
Module 3 · 0 Weeks to complete
Confidence Intervals
Module 4 · 0 Weeks to complete
Linear Regression
Module 5 · 0 Weeks to complete
Bootstrap and Resampling
Module 6 · 0 Weeks to complete
Fee Structure
Instructors
4 Courses
A Dedicated Mathematics Educator and Researcher at TU Delft
Dr. Christophe Smet serves as a distinguished lecturer at TU Delft, specializing in teaching bachelor-level courses in Probability Theory, Statistics, Calculus, and Linear Algebra. He has made significant contributions to mathematics education, notably leading the development of an extensive collection of 535 open-access Linear Algebra exercises that benefit engineering students across multiple disciplines. His academic journey includes teaching experiences at the universities of Leuven and Antwerp, with his doctoral research focused on Classical Analysis, particularly investigating techniques for proving the irrationality of constants. While his initial research centered on orthogonal polynomials and Painlevé equations, his interests have evolved to encompass both particle physics and astrophysics. At TU Delft, he actively participates in creating innovative educational content, including parameterized exercises that allow students to practice with varying values, and has been instrumental in developing comprehensive probability theory courses that cover everything from basic concepts to advanced applications in engineering
4 Courses
A Distinguished Statistician and Mathematics Educator at TU Delft
Dr. Rik Lopuhaä serves as an Associate Professor in the Section of Statistics at TU Delft's Delft Institute of Applied Mathematics, where he has established himself as a leading expert in mathematical statistics. After completing his mathematics studies at the University of Amsterdam, he earned his PhD from TU Delft, specializing in robust multivariate statistical methods. His research focuses on asymptotic theory for statistical methods, particularly in shape-constrained nonparametric methods and robust multivariate analysis. As an educator, he has developed numerous statistics courses and achieved significant recognition as co-author of the widely-used Springer textbook "A Modern Introduction to Probability and Statistics: Understanding Why and How." His teaching portfolio spans various departments at TU Delft and includes collaborations with industry partners. At the university, he actively contributes to academic development through teaching, student supervision, and research activities, while also supporting researchers through the Statistics Helpdesk, where he provides guidance on statistical aspects of research projects.
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