12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Contingency tests are statistical tests used to measure the probability of an observed relationship between two or more variables. They are used to determine whether an observation is statistically significant, i.e., whether the observation is due to chance or whether there is a specific relationship between the variables. For example, contingency tests can be used to examine the relationship between a patient's sex and the result of a laboratory test.
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
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.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Analysis of variance is a statistical data analysis technique used to study the differences between two or more groups. It is often used to find out how much influence each variable has on a particular measured variable. Analysis of variance can also be used to evaluate the strength of the relationship between two or more variables.
12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
The least squares method is a procedure for fitting data to a mathematical function. It is commonly used in statistical analysis to estimate the parameters of a given function from a set of observed data points. The procedure consists of minimizing the deviation between the observed values and the predicted values by adjusting the parameters of the function.