# Statistics and Simulations

This page contains samples of my work demonstrating expertise in programming and statistical analysis. The first example contains code written in Matlab and SAS, plus a sample of my analytical writing style. The second example contains code written in R.

SEM Simulation (Summer 2016)

At New Mexico State University, doctoral students are required to complete one methodological question within their comprehensive exam. Given my particular programming skills and statistical interests, my doctoral committee challenged me to run a simulation exploring competing models within Structural Equation Modeling analyses.

For this project, I chose to generate three population data sets. One set was completely randomized while the other two replicated different relationships between variables. These data sets allowed me to investigate how Structural Equation Modeling anayses function when numerical relationships are and are not present.

Below you will find, (1) the MatLab code used to generate three distinct populations, (2) the SAS code and output for analyze three competing models for each of the three distinct populations, and (3) the written answer submitted to my committee in June of 2016.

Generating Three Distinct Populations – MatLab

SEM Simulation – SAS Code and Output (FULL)

Structural Equation Modeling – Written Document

Simulations and Analyses in R (Spring 2017)

Power Analysis and Regression
The following simulations seeks to estimate the power of a regression analysis given different parameter values (i.e. sigma, slope, and intercept).

Power Analysis and Regression Simulation – R Spring 2017

Checking Assumptions, Violations, Corrections, and Regression Analyses
The following document contains an example from my own research. This data set violated the normality assumption necessary to execute regression analyses. Therefore, I used a box-cox transformation to normalize the data and run regression analyses. I also created  a colorful bar graphs and two scatter plots to visualize the trends and results.

Assumption Violation, Correction, and Data Analysis  