Course Overview
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.
Course Objectives
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.
Course Outline:
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
