Comprehensive Course Curriculum
This repository documents the curriculum, learning objectives, and reading materials for a university-level course on Simulation, Statistical Analysis System (SAS), and R Programming.It was a part of my 3rd year,2nd semester BSc in Statistics course.
Fundamental and advanced concepts used in statistical modeling and computational simulation.
- Meaning and scope of simulation studies
- Discrete and continuous simulation
- Random number generation
- Random variate generation
- Series and convergence
- Simple Monte Carlo integration
- Polynomial and relational functions
- Incomplete gamma and beta functions
- Error function and cumulative probability functions
- Chi-square, t, F, binomial, hypergeometric distributions
- Exponential integrals
- Multidimensional function minimization
Skills for data management, statistical analysis, and structured programming in SAS.
- SAS software environment
- Structure of a SAS program
- DATA step and PROC step
- Data management and permanent datasets
- Importing, input statements, saving and recalling programs
- Core PROC procedures:
PRINT, SORT, FORMAT, MEANS, UNIVARIATE, TABULATE, CORR, SUMMARY, CONTENTS, TRANSPOSE - Statistical procedures:
FREQ, T-TEST, ANOVA, GLM, REG - SAS graphics and PLOT procedures
- Getting into SAS datasets
- Reading and formatting raw data
- Splitting, merging, appending, and updating datasets
- If-conditions and variable selection
- Treatment comparisons
- ANOVA models
- Randomized block and orthogonal designs
- Split-plot and sequential methods
- Regression analysis and diagnostics
Core and applied skills in R for statistical computing, visualization, and modeling.
- Basic R operations
- Lists, data frames, loops, and conditional execution
- User-defined functions
- Data management in R
- Graphics using base R:
scatter plots, bar charts, pie charts, histograms - Regression using R
- ANOVA and all possible regressions
- Model diagnostics
- Fitting statistical distributions
- Simulation using R and case studies
- Ross S. M. (2006). Simulation, 4th Edition, Academic Press
- Der G. & Brian S. E. (2008). A Handbook of Statistical Analyses Using SAS, 3rd Edition, CRC Press
- Cohen Y. & Cohen J. Y. (2008). Statistics and Data with R, Wiley
- Rahman M. S. (2017). R Programming and Data Analysis, Kazi Prokashoni
- Cody R., SAS by Example, SAS Institute
- Cody R., SAS Functions by Example, SAS Institute
- Cody R., SAS Statistics by Example, SAS Institute
- Crawley M. J., The R Book, 2nd Edition, Wiley
- Raithel M. A., How to Become a Top SAS Programmer, SAS Institute
- Graham C. & Talay D. (2013). Stochastic Simulation and Monte Carlo Methods, Springer
- Ripley D. Brian, Stochastic Simulation, Wiley
- Rubinstein R. Y., Simulation and the Monte Carlo Method, Wiley
The curriculum includes a detailed mapping of Course Learning Outcomes (CLOs) to Program Learning Outcomes (PLOs) to align skills in statistical modeling, programming, and data analysis with broader program objectives.
(Insert CLO–PLO table here if needed.)
This repository is intended for academic and instructional use.
You may fork, clone, and adapt the materials with proper attribution.