Publications by Avery Holloman
Multiple Logistic Regression Insights and Applications
Multiple Logistic Regression: Insights and Applications Multiple Logistic Regression: Insights and Applications When I model binary outcomes using multiple predictors, I recognize that extending the logistic regression framework from a single predictor to multiple predictors is crucial for capturing complex relationships. The general model can be ...
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Advertising Effectiveness at BBQ2GO: A Multiple Linear Regression Analysis
Multiple Linear Regression Author Avery Holloman Advertising Effectiveness at BBQ2GO: A Multiple Linear Regression Analysis Abstract As the owner of BBQ2GO, I want to evaluate the effectiveness of advertising expenditures across three channels: social media, direct mail, and newspapers. Using Multiple Linear Regression (MLR), I aim to quantify...
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Estimating the Coefficients of a System of ODEs
Estimating the Coefficients of a System of ODEs Author Avery Marcell Holloman Published December 19, 2024 Introduction When I worked on estimating the parameters of systems of ordinary differential equations (ODEs), I recognized the challenges posed by noisy observations. Unlike traditional methods that rely on extensive observation periods, ...
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Hybrid Regression Analysis for Electrochemical Air Quality Sensors
# Title: Hybrid Regression Analysis for Electrochemical Air Quality Sensors # Abstract # In this project, I explored hybrid regression approaches to calibrate low-cost SO2 electrochemical sensors. Using data from Pahala and Hilo AQ stations, I trained and validated models for predicting SO2 levels under varying environmental conditions. The anal...
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Comparison of Linear Regression with K-Nearest Neighbors
# Abstract # This study evaluated the performance of K-Nearest Neighbors (KNN) and Linear Regression algorithms in predicting power output in wind power generation. # The Linear Regression algorithm demonstrated superior performance with a mean accuracy of 82.15% compared to KNN's accuracy of 79.55%. # The results showed statistical significance...
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Exploring Qualitative and Quantitative Predictors
# Introduction # In this analysis, I explored relationships between various predictors and their impact on balance, a key quantitative variable. The dataset included a mix of demographic, financial, and behavioral variables such as age, number of credit cards, education, income, credit limit, and student status, among others. Using visualizations ...
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Exploring Customer Sales Data
# ABSTRACT # This project is a comprehensive statistical exploration of customer sales data. # My primary goal was to understand sales trends, evaluate revenue patterns, and assess # the impact of discounts on revenue. I used statistical techniques such as tabulations, # visualizations, and aggregate calculations to uncover meaningful insigh...
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Qualitative Predictors
# Exploring Qualitative Predictors in Medical Data Regression Models # In my journey of analyzing medical data, I find it intriguing to observe how regression models can seamlessly integrate both quantitative and qualitative predictors. Real-world datasets, especially those in the medical field, often present a mix of variable types, which opens u...
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Logistic Regression
# Logistic regression is a powerful tool I chose to analyze the probability of shipment delays in my warehouse shipping data. # I noticed that a linear regression model would struggle with predicting probabilities since it can produce values outside the [0, 1] range. # To address this, I applied logistic regression to ensure meaningful and inte...
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Overview of Classification
# The linear regression model assumes that the response variable Y is quantitative. # But in many situations, the response variable is instead qualitative. For example, in this analysis, # the response variable is reactivity, which is qualitative, as it categorizes elements as either reactive (Yes = 1) # or non-reactive (No = 0). Often, quali...
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