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  • Determining the Drivers for Higher Credit Score. The operations unit would like to be informed of the variables that could explain good credit score among borrowers. In collaboration with the same unit the data scientist has been given access to their data set. The data set provided involves all the records of companies which bears variables relevant to credit scoring such as Annual Revenue, Profit Margin, Number of Employees, Years In Operation, Previous Payment Defaults, Avg Monthly Debt Obligation, Current Loan Amount, Collateral Value, Financial Statement Quality, Type of Industry, and Region. Given that most of the assumptions of OLS regression were missed out, Tukey Robust Regression was employed for the analysis. Findings show that Annual Revenue, Collateral Value, Current Loan Amount, Years in Operation, Financial Statement Quality, Profit Margin and Presence of Previous Defaults have higher variance explained on Credit Risk Score. Those with lower annual revenue, lower collateral value, shorter years in operation, poor financial statement quality, and lower profit margin are likely to have higher risk score. Moreover, those with higher current loan amount and those with records of defaults in payment are also likely to have higher risk scores.
  • Regression Analysis on Car Variables and its Mileage. In this report we look into a collection of cars and explored on the relationship between a set of variables and miles per gallon as the the outcome. Inferential analysis show that manual (24.39) is better compared to automatic (17.15) in terms of mpg. However, results of the regression analysis using the best model show that when significant predictors such as cylinder(cyl), horsepower(hp) and weight(wt) are included in the model, the type of transmission seems to be insignificant (p > 0.05) even though shifting from auto to manual increases mpg by 1.81.
  • Exploring the Probable Predictors of Hospital Stay: The results on the recent satisfaction survey of the clients show that many had concerns on the length of hospital stay and claim that such is attributed by the documentation processing prior to discharge. Further investigation is conducted with random collection of data from a computed sample size. The goal of the analysis is to confirm whether documentation processing (or any other variables) contribute much to the length of hospital stay (in minutes).