Aspen Gulley

Abstract: This article provides an overview of structural equation modeling (SEM), a statistical method widely used in various domains. The article explains what SEM is, its components, types, the mathematics, as well as the data, assumptions, and challenges associated with it. It also covers the five steps of building a SEM, an example of the… Continue reading

Using measures of transformational leadership, this research seeks to predict levels of employee engagement and employee satisfaction. Employee engagement “describes the level of enthusiasm and dedication a worker feels toward their job” (Smith, 2020, para. 1). Engaged employees “care about their work and about the performance of the company, and feel that their efforts make… 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

In this analysis I am going to build a Bayesian Network in R based on medical data to identify the likelihood of six possible diseases based on clinical presentations in infants. The idea and data for this analysis came from the CHILD network created by David J. Spiegelhalter, A. Philip Dawid, Steffen L. Lauritzen, Robert… 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

# Visualizing Starbucks locationsimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns; sns.set()import cartopy.crs as ccrsfrom mpl_toolkits.basemap import Basemapfrom matplotlib.colors import Normalize%matplotlib inline# Load Starbucks datadf=pd.read_csv(‘Starbucks.csv’)# View the dataframedf.head() # Rank the top 10 countries with most number of stores top10 = df.Country.value_counts().head(10)top10 # Plot top 10 countriesfig, ax=plt.subplots(figsize=(10,5))ax=sns.barplot(y=top10.index, x=top10.values, ax=ax,… 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

The NCI microarray data contains expression levels on 6830 genes from 64 cancer cell lines. library(ISLR)library(kohonen)nci<-NCI60$datalabels<-as.data.frame(NCI60$labs)labelstable(labels)nci.scaled <- scale(nci) #fit a SOMset.seed(21)som.grid <- somgrid(xdim = 4, ydim = 4, topo = “hexagonal”) nci.som <- som(nci.scaled, grid = som.grid, rlen = 1000)nci.som$codescodes <- nci.som$codes[[1]]nci.som$unit.classiftable(nci.som$unit.classif) quartz()plot(nci.som, type = “changes”, main = “NCI Data”)#plot vector of changes, the average… Continue reading

Part 1: Use Machine Learning to Predict a Death Event Due to Heart Failure The goal of this analysis is to compare machine learning methods when predicting a death event due to heart failure. The predictor variables of a death event include age, anaemia, creatinine phosphokinase, diabetes, ejection fraction, high blood pressure, platelets, serum creatinine,… 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