Thoroughly understand statistical concepts, apply them in R and interpret the results correctly with maximum validity
What you will learn
Thorough understanding of basic and advanced statistical theory
How to perform simple and advanced statistical analyses in R
How to fully and correctly interpret the results
How to correctly present the results in papers or reports
How to get reproducible results with every type of analysis carried out in the course
How to make accurate predictions based on your regression results
How to deal with real issues in statistical modeling
The concepts are made simple and the understanding about them is at an advanced level once you finish the course
Description
This course takes you from basic statistics and linear regression into more advanced concepts, such as multivariate regression, anovas, logistic and time analyses. It offers extensive examples of application in R and complete guidance of statistical validity, as required for in academic papers or while working as a statistician.
Statistical models need to fulfill many requirements and need to pass several tests, and these make up an important part of the lectures.
This course shows you how to understand, interpret, perform and validate most common regressions, from theory and concept to finished (gradable) paper/report by guiding you through all mandatory steps and associated tests.
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Taught by a university lecturer in Econometrics and Math, with several international statistical journal publications and a Ph.D. in Economics, you are offered the best route to success, either in academia or in the business world.
The course contents focus on theory, data and analysis, while triangulating important theorems and tests of validity into ensuring robust results and reproducible analyses. Start learning today for a brighter future!
English
language
Content
Add-On Information:
- Course Overview
- This curriculum serves as a comprehensive bridge between abstract mathematical theory and the pragmatic world of data programming, ensuring that students do not just run scripts but truly comprehend the underlying logic of their operations.
- The course adopts a first-principles approach to data science, prioritizing the “why” behind every test and formula before moving into the execution phase within the R environment.
- Participants will explore the philosophical foundations of statistical inference, learning to distinguish between mere correlation and substantive causation through rigorous analytical frameworks.
- The journey begins with foundational data distributions and traverses through complex multivariate landscapes, providing a holistic view of the statistical lifecycle from raw data to actionable insight.
- Special emphasis is placed on the ethical implications of data manipulation, teaching students how to maintain objectivity and avoid common pitfalls like p-hacking or data dredging.
- Requirements / Prerequisites
- A functional computer (Windows, macOS, or Linux) with the latest version of R and RStudio installed, though no prior coding experience is strictly necessary as the course builds syntax from the ground up.
- A basic understanding of high-school-level algebra is recommended to follow the derivation of certain statistical parameters and logic flows.
- A curious and analytical mindset is the primary requirement, as students are encouraged to question assumptions and look beyond the surface level of their datasets.
- Willingness to engage with quantitative problem-solving and a commitment to practicing coding exercises alongside the theoretical lectures.
- Skills Covered / Tools Used
- Proficiency in the Tidyverse ecosystem, specifically utilizing packages like dplyr for data manipulation and ggplot2 for creating high-impact, publication-quality statistical visualizations.
- Mastery of diagnostic checking, where students learn to use residual plots, leverage statistics, and influence measures to validate their model assumptions.
- Implementation of non-parametric techniques for situations where data violates standard normality assumptions, ensuring flexibility in real-world scenarios.
- Deep dive into Probability Theory and Sampling Distributions, enabling a better grasp of the Central Limit Theorem and its practical application in estimation.
- Advanced use of RMarkdown for creating dynamic, integrated reports that blend executable code with explanatory text for professional delivery.
- Benefits / Outcomes
- Development of a statistical intuition that allows for the selection of the most appropriate analytical tests for any given research question or business problem.
- Enhanced data literacy, empowering students to critically evaluate the statistical claims made in media, scientific journals, and corporate presentations.
- The ability to transform “messy” real-world data into structured formats suitable for rigorous mathematical modeling and hypothesis testing.
- A significant boost in professional credibility, as the course equips learners with the vocabulary and technical skills required to lead data-driven projects in any industry.
- The creation of a reusable code library that students can carry forward into their future careers to streamline their workflows and maintain high standards of analysis.
- PROS
- The course avoids the “black-box” syndrome by explaining the internal mechanics of R functions rather than just teaching which buttons to click.
- Highly versatile applications across various fields including healthcare, finance, social sciences, and engineering.
- Focuses on long-term knowledge retention by utilizing intuitive analogies and practical examples instead of rote memorization of formulas.
- CONS
- The technical depth and rigorous nature of the content may require multiple viewings of certain sections for those who have no prior background in quantitative reasoning or computer programming.
Introduction to the course
Introduction
Single Linear Regression
Install R, RStudio and Basic Functionality
Basics of Linear Regression
Basics of Linear Regression Ctnd
Linear Regression Analysis
Linear Relationships
Line of Best Fit, SSE and MSE
Linear Regression Analysis Ctnd
Regression Results and Interpretation
Predicting Future Profits
Statistical Validity Tests
Statistical Validity Discussion
Additional Resources
Single Linear Regression
Multiple Regression
Multiple Linear Regression
Importing the data
Correlation Matrix and MLR
MLR Results and ANOVA
The Best Model?
Interaction Terms and Validity Testing
ANOVA and Predictions