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PROJECT OVERVIEW This project provides a detailed exploration of structural equation modeling, from theoretical foundations and assumptions through model specification, evaluation, comparison, and implementation in R. The work examines how SEM can represent relationships among observed and latent variables, how models are identified and estimated, and how researchers evaluate fit and refine models. It also… Continue reading

PROJECT OVERVIEW This original research project examines whether transformational leadership measures can predict employee engagement and employee satisfaction. The analysis develops a research objective and hypotheses, prepares a modified survey dataset, and compares multiple predictive approaches. It evaluates decision trees, random forests, logistic regression, and support vector machines alongside Pearson correlations to examine both predictive… Continue reading

Today I will explore several insights derived from my analysis. The focal points of discussion will encompass a range of subjects, including the assessment of browser and device compatibility with the webpage, identification of challenges encountered by users employing mobile devices, the concerning decline in add-to-cart rates, as well as the overall stagnant nature of… Continue reading

PROJECT OVERVIEW This project builds a Bayesian Network in R to reason about the likelihood of possible diseases from clinical presentations in infants. The analysis starts by defining the network structure from an established model, parameterizing it with medical data, visualizing the resulting network, and performing evidence-based probability queries. The goal is to show how… Continue reading

A low birth weight dataset records 100 births and whether or not these babies experiences a germinal matrix hemorrhage (grmhem). The babies’ 5 minute apgar score is recorded (apgar5) and also whether or not the mother had toxemia (tox) during her pregnancy. The odds ratio of whether a baby experienced a germinal matrix hemorrhage associated… Continue reading

SELECTED PROJECT Supply Chain Analytics Mapping coffeehouse locations, understanding customers with cohort analysis, RFM analysis, and K-means clustering, and exploring top-quality coffee producers for supplier recommendations. Overview This project applies multiple analytical approaches to a supply chain and customer analytics problem. It combines geographic visualization, cohort analysis, RFM analysis, and K-means clustering to understand customer… Continue reading

The Kaplan-Meier survival function gives the probability of surviving past time, t: 86 bladder cancer patients had tumors removed. After removal, these patients were separated into two groups, a placebo group (group 0), and a drug Thiopeta treatment group (group 1). The variable time in this dataset represents how many months until a tumor reoccurred… Continue reading

PROJECT OVERVIEW This project examines high-dimensional gene-expression data from cancer cell lines using unsupervised learning and dimensionality-reduction methods. The analysis compares self-organizing maps, hierarchical clustering, and principal component analysis to identify structure in the NCI microarray data. It evaluates how different approaches group cancer cell lines and considers the tradeoffs between complex clustering workflows and… Continue reading

PROJECT OVERVIEW This project compares machine-learning approaches for predicting death events associated with heart failure and diabetes. The analysis uses clinical datasets to build, evaluate, and compare several predictive models. It examines decision trees, random forests, bagging, boosting, k-nearest neighbors, and logistic regression, with attention to test error, variable importance, model performance, and interpretability. What… Continue reading

My goal with this analysis was to predict chronic kidney disease based on 24 attributes. The dimensions of the data are 400 by 25, including the dependent variables. There are 150 observations of patients who do not have chronic kidney disease and 250 observations of patients with chronic kidney disease. I hoped to create a… Continue reading

In the first part of this analysis I am going to show an example of hierarchical clustering and how correlation can help aid in the understanding of dendrogram results. I will then explore some star plots. State data released from the US department of Commerce, Bureau of the Census is available in R. I will… Continue reading

In the first part of this analysis I am going to show an example of hierarchical clustering and how correlation can help aid in the understanding of dendrogram results. I will then explore some star plots. State data released from the US department of Commerce, Bureau of the Census is available in R. I will… Continue reading

For this tutorial, I am going to use the transaction Income dataset from the R package arules. This dataset comes from the website for the book The Elements of Statistical Learning. Chapter 14 has information about association rules. Here is a link and a citation for that book: Hastie, T., Tibshirani, R. & Friedman, J. (2001) The Elements… Continue reading

The income data used for this analysis can be found under the marketing database provided by The Elements of Statistical Learning by Hastie, T., Tibshirani, R., & Friedman, J. H.. Here is the link: https://hastie.su.domains/ElemStatLearn/data.html Attribute information includes household income, sex, marital status, age, education, occupation, how long the person has lived in the San Francisco/Oakland/San Jose area… Continue reading

Hypothesis Testing: ANOVA The following is a compilation of a series of ANOVA tests conducted over the course of a few years. Analysis of Variance (ANOVA) is a statistical technique used to compare the means of groups to determine if there are any statistically significant differences between them. It helps in understanding whether there are variations… Continue reading