Multivariate Analysis Commons^™

Modeling And Solving The Outsourcing Risk Management Problem In Multi-Echelon Supply Chains, Arian A. Nahangi

Master's Theses

Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods.

This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Scale-Invariant Geometric Data Analysis (Sigda), Marina Girgis, Max Robinson

STAR Program Research Presentations

The purpose of this research is to introduce a new data analysis method called Scale Invariant Geometric Data Analysis (SIGDA). SIGDA has been shown to be more informative than more common data analysis methods, such as Principal Component Analysis (PCA). SIGDA is used to visualize complex data sets in a way that accurately preserves data patterns and behavior. SIGDA is designed to preserve relative ratios in a numerical matrix, and the number of entries has to be more than the total number of rows and columns. Our research involved providing a simple explanation of SIGDA's mathematical process—simple enough for the …

Reaching The Gold Standard: Assessing Driving Ability Among Student And Expert Drivers, Alyssa Davis

Statistics

No abstract provided.

Blunt Impact Performance Evaluation Of Helmet Lining Systems For Military And Recreational Use, Jaclyn Siniora, Ryan Taylor, Darren Suey

Industrial Technology and Packaging

With the increasing problem in collegiate athletes experiencing injuries to the brain, different helmet liners where put to the test to see which liner provided athletes the greatest protection under specific conditions.

This senior project evaluates five different liners in football helmets. Each of the helmet liners were tested at three different temperatures: hot, cold, and ambient. Each helmet had seven different impact locations which were put to the test. The project was designed to be used to test ACH military combat liners as well. Due to shipping bottle necks the ACH combat liners have been left to future Cal …

Analysis Of Dietary Patterns Over Freshman Year Of College, Chelsea Lofland

Statistics

This analysis is an investigation of changes in Cal Poly students’ eating habits over freshman year. The motivation behind this was an interest in college students’ lifestyles; college is the first time most students live on their own and it can be an important maturation period. College is stressful, exciting, liberating, and terrifying all at the same time. This distinctive life experience, along with my desire to handle big and messy data, led me to this research question.

The response variable analyzed was food consumption and the explanatory variables were: sex, race, quarter, food group, stress, exercise, BMI, sleep quality …

Manova: Type I Error Rate Analysis, Christopher Dau Wei Ling

Statistics

No abstract provided.

Statistical Analysis Of Texas Holdem Poker, Daniel Bragonier

Statistics

Gathered lifetime online Poker data for Mike Linn. Attempted to analyze data to obtain information to maximize profit. Techniques included Univariate Analysis, Regression analysis, Anova analysis, Logistic Regression, and outlier Analysis. After the analysis, nothing of supreme importance or sustenance was found. Encountered issues with too much power. Results lead to plenty of statistical significance, but little practical significance. Results showed that the data did not provide all the answers that were being sought after, but there was some value in examining the data in a strict statistical manner.

Manova: Type I Error Rate Analysis, Kyle Wesley Gasperik

Statistics

Multivariate analysis of variance (MANOVA) is most commonly used in the field of bio-statistics. Throughout this paper I conduct numerous simulations that help analyze how robust the MANOVA procedure is against its assumptions. Using Type I error rate as my measure of error, I used the R software to graph my results. The main assumption that is focused on is the equal covariance matrix assumption, which we introduce correlation between variables to see how well the MANOVA procedure performs. Overall, 70 simulations were ran, and 10 functions were created to perform all of the analysis.

Investigation Of Mlb Data With Multivariate Statistics, Vincent Milano

Statistics

A statistical study was performed in order to explore the relationships of the offensive player statistics for every player in the 2008 Major League Baseball season. The purpose of the study was to explore various multivariate statistical methods within the data set.

The offensive variables in the study are: games, at-bats, runs, hits, singles, doubles, triples, homeruns, extra base hits, runs batted in, total bases, walks, strikeouts, stolen bases, times caught stealing, on-base percentage, slugging percentage, on-base plus slugging percentage, and batting average. All variables are season totals except for onbase percentage, slugging percentage, on-base plus slugging, and batting average. …