Training on Mastering Statistical Analysis with R

Statistical analysis is foundational for extracting meaningful insights from data, driving informed decisions across industries. This 10-day course on Mastering Statistical Analysis with R provides participants with both theoretical understanding and practical skills to analyze complex datasets using the R programming language.

Participants will gain hands-on experience in data manipulation, hypothesis testing, regression analysis, and multivariate techniques, applying statistical methods to real-world problems. The course emphasizes practical applications, enabling learners to interpret results, validate findings, and communicate insights effectively.

By the end of the training, participants will be capable of designing, executing, and interpreting robust statistical analyses, leveraging R to support evidence-based decisions in business, research, policy, and other professional domains.

Duration

10 Days

Who Should Attend

• Aspiring Data Analysts and Data Scientists
• Business and Financial Analysts
• Researchers and Academics
• IT and BI Professionals involved in analytics
• Policy Analysts and Decision-Makers
• Professionals seeking practical skills in statistical analysis using R

Organizational Impact

• Improve the integrity and credibility of data-driven decisions through rigorous statistical analysis.

• Boost operational efficiency by reducing time spent on manual analysis and reporting.

• Foster a data-literate culture to uncover insights and make evidence-based decisions.

Personal Impact

• Gain in-demand skills for careers in data analysis, research, and data stewardship.

• Progress toward senior analytical or research roles.

• Ensure work quality and reliability with reproducible findings.

• Build confidence to lead and manage complex quantitative projects.

By the end of this course, participants will be able to:

• Understand the fundamentals of R programming for statistical analysis.
• Perform data manipulation and cleaning in R.
• Apply basic and advanced statistical methods to analyze data.
• Conduct hypothesis testing and interpret the results.
• Implement regression analysis, including linear and logistic regression.
• Utilize R for multivariate analysis techniques such as PCA and clustering.
• Create and interpret statistical plots and graphs in R.
• Analyze time series data using R.
• Develop reproducible reports and presentations of statistical analyses.
• Apply statistical analysis skills to real-world data sets and research questions.

Module 1: Introduction to R and RStudio

• R as a statistical computing environment
• RStudio IDE: interface and basic functionalities
• Data types and structures in R (vectors, matrices, data frames)
• Basic data manipulation and subsetting

Module 2: Data Import and Export

• Importing data from various formats (CSV, Excel, SPSS, etc.)
• Exporting data to different formats
• Data cleaning and preprocessing

Module 3: Exploratory Data Analysis (EDA)

• Summary statistics (mean, median, mode, standard deviation, etc.)
• Data visualization (histograms, box plots, scatter plots, etc.)
• Correlation and covariance
• Outlier detection

Module 4: Probability and Distributions

• Probability concepts and rules
• Discrete and continuous probability distributions
• Normal distribution and its properties
• Sampling distributions

Module 5: Hypothesis Testing

• Hypothesis testing framework
• One-sample and two-sample t-tests
• Chi-square test for independence
• ANOVA (one-way and two-way)

Module 6: Linear Regression

• Simple linear regression
• Multiple linear regression
• Model evaluation (R-squared, adjusted R-squared, F-test)
• Model diagnostics

Module 7: Logistic Regression

• Logistic regression model
• Odds and logit
• Model evaluation (confusion matrix, ROC curve, AUC)

Module 8: Non-parametric Methods

• Rank-based tests (Wilcoxon, Kruskal-Wallis)
• Correlation analysis (Spearman, Kendall)

Module 9: Advanced Topics in Statistics

• Time series analysis
• Survival analysis
• Bayesian statistics
• Machine learning with R

Module 10: Data Visualization with R

• Advanced data visualization techniques
• Creating interactive plots
• ggplot2 package for advanced visualization