The goal of this project was to analyze factors in a business' success. The following metrics were used and defined relatively as follows:
- NOE = Number of Employees - Global (Latest)
- R_D = R&D Expense [LTM] ($USDmm, Historical rate)
- COE = Cost Of Revenues [LTM] ($USDmm, Historical rate)
- TOE = Total Operating Expenses [LTM] ($USDmm, Historical rate)
- EPS = Basic EPS [LTM] ($USD, Historical rate)
- TEV = Total Enterprise Value [My Setting] [Latest] ($USDmm, Historical rate)
- EBITDA = EBITDA [LTM] ($USDmm, Historical rate)
In successful analysis of the fields above, it may be possible to see that fields such as research and development expenses are strongly correlated with a business' EBITDA, EPS, or TEV or whether it is negatively correlated and/or more closely related to expenses (TOE, COE).
Through running regression analysis, it can better correlate to see how certain fields may influence each other and what might relate more than others. In this way, it may be possible to find an algorithm to model a business' expense model in relation to their R&D, number of employees, and how much they should be spending for Cost of Revenues, and the Total Operating Expenses.