Publications by Avery Holloman
Principal Component Analysis Variance in Logistic Warehouse Shipping
Principal Component Analysis Variance in Logistic Warehouse Shipping When I analyzed the scree plot and cumulative explained variance for logistic warehouse shipping data, I noticed some key trends. The first principal component (PC1) explained approximately 65% of the variance. This told me that most of the variability in shipping data is driv...
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PCA Results on Data Scientist Skills
My Interpretation of PCA Results on Data Scientist Skills Using Statistics When I analyzed the scaled and unscaled PCA plots for data scientist skills, I noticed distinct patterns in how the variables contributed to the principal components. In the scaled PCA plot, the first principal component (PC1) accounted for approximately 55% of the varia...
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Survival Analysis of Autonomous Vehicle Components
Survival Analysis of Autonomous Vehicle Components When I analyzed the reliability of autonomous vehicle components using survival analysis, I focused on cleaning and preparing a simulated large dataset. I know that reliability depends on consistent and robust data, so I took a systematic approach to data preparation. I started by loading the d...
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Addressing Missing Data in Automation and Robotics Production Lines
# Load the necessary libraries library(missMDA) # I loaded this library because it provides PCA-based imputation methods for handling missing data. library(ggplot2) # I used ggplot2 because it allows me to create detailed and customizable plots for data visualization. # Step 1: Simulate production line data set.seed(123) # I set a seed for ...
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An Empirical Comparison of Logistic Regression, Naïve Bayes, and KNN for Credit Card Fraud Detection
An Empirical Comparison of Logistic Regression, Naïve Bayes, and KNN for Credit Card Fraud Detection Author Avery Holloman Comparing Logistic Regression, Naïve Bayes, and KNN for Credit Card Fraud Detection Introduction When I think about financial fraud, it always strikes me how much damage it causes—not just to individuals but to the ent...
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Logistic Regression Predicting Contamination Risks
Logistic Regression Predicting Contamination Risks Author Avery Holloman Logistic Regression Predicting Contamination Risks Abstract In my quest to explore sustainable energy solutions, I found landfill-based wind turbine energy systems to be an intriguing approach. These systems effectively merge landfill gas and wind energy to generate elect...
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Financial Insights Through Multiple Regression
Financial Insights Through Multiple Regression Author Avery Holloman Financial Insights Through Multiple Regression In the world of financial analytics, I often rely on multiple linear regression to uncover the relationships between variables that influence key outcomes. The elegance of this approach lies in its ability to simultaneously evalua...
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Quadratic Discriminant Analysis for eCommerce Logistic Shipping
Quadratic Discriminant Analysis Author Avery Holloman Quadratic Discriminant Analysis for eCommerce Logistic Shipping In my exploration of classification techniques for eCommerce logistic shipping, I found Quadratic Discriminant Analysis (QDA) particularly intriguing. While Linear Discriminant Analysis (LDA) assumes that observations within eac...
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Incorporating Qualitative Predictors in Regression Analysis
Incorporating Qualitative Predictors in Regression Analysis Author Avery Holloman Incorporating Qualitative Predictors in Regression Analysis When I work with regression models, I often encounter variables that go beyond simple numerical measures and delve into qualitative aspects. These qualitative variables—often referred to as categorical ...
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Logistic Regression: Modeling the Probability of Default
Logistic Regression Modeling the Probability of Default Author Avery Holloman Logistic Regression: Modeling the Probability of Default Abstract Logistic regression is a powerful method for modeling binary outcomes. Unlike linear regression, logistic regression uses the logistic function to ensure predicted probabilities stay within the range [...
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