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
Customized Training
This training can be tailored to your institution needs and delivered at a location of your choice upon request.
Requirements
Participants need to be proficient in English.
Training Fee
The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.
Certification
A certificate from Ideal Workplace Solutions is awarded upon successful completion.
Accommodation
Accommodation can be arranged upon request. Contact via email for reservations.
Payment
Payment should be made before the training starts, with proof of payment sent to [email protected].
For further inquiries, please contact us on details below:
