Applied Statistical Analysis and Modeling with R
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Next Start
September 28, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,250
Exclusive 16% VATOther Dates
October 26, 2026
November 23, 2026
November 23, 2026
Course Description
This professional course provides a practical foundation in statistical analysis and modeling using R. Participants will learn how to describe and summarize data, work with probability and statistical distributions, perform statistical inference, conduct hypothesis tests, and build statistical models using R's powerful statistical computing ecosystem.
The curriculum progresses from descriptive statistics and probability to statistical inference, correlation, regression, and advanced statistical modeling. Participants will apply tidyverse packages (dplyr, tidyr, ggplot2), as well as base R, stats, and broom to perform statistical computations, test hypotheses, fit models, evaluate model assumptions, and interpret statistical results.
Prerequisite note: This course does not teach R or the tidyverse from scratch. Participants are expected to be familiar with these tools; the curriculum focuses on their practical application to data analysis.
Course Outline
This course is structured into 6 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Calculate and interpret descriptive statistics using R.
- Detect and handle outliers and missing values with tidyverse.
- Create professional summary tables and pivot tables from grouped data using dplyr and tidyr.
- Export styled tables to HTML, Excel, PDF, and as images using knitr, kableExtra, and huxtable.
- Construct and interpret confidence intervals for means and proportions.
- Perform and interpret statistical hypothesis tests using base R and stats.
- Calculate and interpret Pearson and Spearman correlation coefficients.
- Build and interpret simple and multiple linear regression models using lm() and broom.
- Evaluate regression models using R², adjusted R², residuals, and diagnostic methods.
- Perform one-way ANOVA and analyze differences between group means.
- Conduct post-hoc tests and multiple comparisons after ANOVA using TukeyHSD and emmeans.
- Build, interpret and evaluate logistic regression models for binary outcomes using glm().
Career Benefits
- Build a strong, practical foundation in statistical analysis using R's scientific ecosystem.
- Learn to apply statistical methods to real-world datasets and business problems.
- Develop practical skills in statistical inference, hypothesis testing, and model evaluation.
- Gain hands-on experience with tidyverse, stats, broom, and ggplot2.
- Learn to build, interpret, and evaluate statistical models with confidence.
- Master professional table presentation techniques for reports and publications using knitr and kableExtra.
- Strengthen quantitative reasoning skills for data-driven professional decision-making.
- Establish a solid foundation for advanced study in data science and machine learning.
Who Should Attend
- Data Analysts — Professionals seeking to strengthen their statistical analysis and reporting capabilities using R.
- Data Scientists — Practitioners looking to solidify their statistical foundations and modeling skills in R.
- Researchers & Academics — Individuals applying statistical methods to research data and publishing results.
- Business Analysts — Professionals using statistics to support data-driven business decisions.
- Economists & Social Scientists — Professionals working with quantitative data and statistical models.
- R Developers — Programmers looking to apply statistical methods and create professional tables in R.
- Students — Learners developing practical statistical and modeling skills for their careers.
- Data Professionals — Anyone who needs to analyze, model, interpret, and present data effectively using R.
Prerequisites & Requirements
Prerequisites
- Basic computer literacy and familiarity with file management.
- Basic high school mathematics (algebra, functions) is required.
- General R programming knowledge is required.
- Tidyverse (dplyr, tidyr, ggplot2) knowledge is required.
- Basic algebra and equation-solving skills are recommended (but not mandatory).
- A willingness to work with quantitative data and embrace statistical concepts.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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