12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
A significant relationship is a statistically measurable relationship between two or more variables, the result of which is statistically significant. This means that the relationship between the variables is more than just coincidental and that it really exists. A significant relationship means that a change in one variable is associated with a change in the other variable.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
A trend is a general direction or development that is developing in a particular area or industry. Trends can relate to many areas, including fashion, technology, business, culture, media and more. Trends can develop over a period of time or come and go quickly.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
A hypothesis test is a statistical test used to determine whether a particular hypothesis is true or false. It is a procedure for testing hypotheses to see whether or not they can be supported by data. Usually, hypothesis tests are used to determine the probability that a certain outcome will occur.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Regression analysis is a mathematical technique used to predict future outcomes and determine the relationships between different variables. It can be used to predict the effect of a particular influencing factor on another variable or to determine which variables contribute most strongly to a particular metric. Applications of regression analysis include predicting sales volumes, determining prices, and determining credit risk.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
The Gauss-Markov assumptions are a group of assumptions used in linear regression analysis. They include the assumption that the influencing factors (the predictor variables) are independent of each other, the relationship between the influencing factors and the dependent variable is linear, the variance of the dependent variable is constant, and the residuals are normally distributed. The Gauss-Markov assumptions form the basis for linear regression analysis.