12/06/2022 | By: FDS
Multivariate analysis is a method of statistical analysis that examines multiple variables simultaneously to identify relationships. It is commonly used in the fields of economics, psychology, sociology, and other social sciences to examine complex relationships among multiple variables. Multivariate analysis allows scientists, businesses, and organizations to identify patterns and influences that help them understand and predict behaviors and trends.
12/06/2022 | By: FDS
Multicollinearity is a statistical phenomenon in which there is variable correlation between multiple variables in a model. It occurs when two or more variables are highly correlated with each other, which can lead to bias in estimates. Multicollinearity can affect the precision of estimates and make it more difficult to determine the individual effects of a variable on the model outcome.
12/06/2022 | By: FDS
Resampling is a method for studying the stability of statistics and estimates based on an existing data set. It is used to validate the behavior of estimates by recombining known data to produce new data. This allows the robustness and variance of certain estimates to be determined.
12/06/2022 | By: FDS
A parameter is a value or argument passed to a function, algorithm, or programming language to achieve a specific behavior or result. Parameters help increase the flexibility of the function, algorithm, or programming language by allowing the same program to be executed with different input values to achieve different results.
12/06/2022 | By: FDS
A random variable is a variable whose value is random for an outcome from an experiment or other process. Random variables can take on discrete, continuous, or combined values. Examples of random variables are the number of copies of a certain book that a publisher sells, the number of students who receive a certain course grade, or the number of goals that a certain soccer team scores during a season.