12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Exploratory factor analysis is a statistical technique that identifies behavioral relationships and dependencies among variables in a data set. It is often used to understand the structure of complex data. It allows the user to group variables that produce similar results to make them easier to analyze. It can also be used to understand the concept of reduction of variables, reducing the number of variables that must be considered in the analysis.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Monte Carlo simulations are mathematical techniques that make it possible to simulate complexity and uncertainty in models to obtain unexpected results. They are commonly used in financial models to predict the impact of risk and uncertainty on the outcome of an investment. They use a combination of multiple random variables to produce a range of outcomes that can then be analyzed to make the best possible decision.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Bootstrapping is a process that allows a company to start and operate a business with a limited amount of funding and resources. It is a way to build and operate a business without the need for outside investors or loans. Instead, savings, personal investment, and sales are used to fund the business.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
The coefficient of variation (also known as the coefficient of variance) is a measure of the dispersion of a group of values. It is calculated by dividing the standard deviation by the arithmetic mean of the group. The coefficient of variation provides a quick and easy way to measure the dispersion within a group of values. The higher the coefficient of variation, the greater the dispersion of the values.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Structural equation modeling (SEM) is a modern form of statistical analysis used to study relationships between variables in complex data sets. This method allows researchers to create a model that describes the influence of different variables on the behavior of an individual or group. SEM can be used to improve research results by testing hypotheses about relationships between variables, considering multiple variables simultaneously.