Statistical Models for Predicting College Success

Colleges base their admission decisions on a number of factors to determine which applicants have the potential to succeed. This study utilized data for students that graduated from Florida International University between 2006 and 2012. Two models were developed (one using SAT as the principal explanatory variable and the other using ACT as the principal explanatory variable) to predict college success, measured using the student’s college grade point average at graduation. Some of the other factors that were used to make these predictions were high school performance, socioeconomic status, major, gender, and ethnicity. The model using ACT had a higher R^2 but the model using SAT had a lower mean square error. African Americans had a significantly lower college grade point average than graduates of other ethnicities. Females had a significantly higher college grade point average than males.

Predicting Lodging Manager Annual Salary Based on Metropolitan Statistical Area Attributes and Lodging Industry Performance: Exploring the Concept

Using multiple regression analysis, lodging managers’ annual mean salaries in 143 Metropolitan Statistical Areas (MSA) within the U.S. were analyzed to identify what relationships existed with variables related to general MSA characteristics, along with the lodging industry’s size and performance. By examining the relationship between these variables, the authors predict the long-term possibility of predicting lodging industry managers’ salaries. These predictions may have an impact on financial performance of an individual lodging property or organization. Through this paper, this concept was applied and explored within U.S. MSAs. These findings may have value for a variety of stakeholders, including human resources practitioners, the hospitality education community, and individuals considering lodging management careers.

Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes

Microarray platforms have been around for many years and while there is a rise of new technologies in laboratories, microarrays are still prevalent. When it comes to the analysis of microarray data to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. When it comes down to choosing which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is usually SAM or samroc but when the data tends to be skewed, the power of these methods decrease. With the concept that the median becomes a better measure of central tendency than the mean when the data is skewed, the tests statistics of the SAM and fold change methods are modified in this thesis. This study shows that the median modified fold change method improves the power for many cases when identifying DE genes if the data follows a lognormal distribution.