Training on Mastering Statistical Analysis with R

This course provides a comprehensive introduction to statistical analysis using the R programming language, a powerful tool for data analysis and visualization. Participants will learn how to manipulate data, conduct a variety of statistical tests, and interpret results in R. The course covers both basic and advanced statistical techniques, including hypothesis testing, regression analysis, and multivariate analysis, with a focus on real-world applications. By the end of the course, participants will be equipped to perform statistical analyses with confidence, making data-driven decisions in their respective fields.

Course Duration

10 Days

Who Should Attend

• Data analysts and statisticians looking to enhance their skills using R.
• Researchers and academics who require statistical analysis in their work.
• Business analysts who need to perform data-driven decision-making.
• Graduate students and professionals in social sciences, economics, and life sciences.
• Individuals with basic programming knowledge looking to learn statistical analysis in 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