12/06/2022 | By: FDS
Data visualization is a technique that allows complex data sets and information to be presented and interpreted in a visual form. These visualizations can be created as charts, maps, tables, networks, graphs, infographics, or other visual forms. They can help us see trends, patterns, and relationships in data, and they allow us to more easily understand and analyze the information.
12/06/2022 | By: FDS
The chi-square test (also chi-square test or chi-square test) is a statistical test used to test hypotheses about the independence of two characteristics. It is often used to test the significance of an observational or experimental data analysis. The chi-square test is used to find out whether a particular set of observations or measurements is significantly different from the expected value. The test is based on comparing the empirical probabilities of a particular outcome with the theoretical probability of the same outcome.
12/06/2022 | By: FDS
The Anova test is a statistical procedure used to examine differences between two or more groups. It is often used in research to determine whether mean values differ significantly between two or more groups. The Anova test can also be used as part of a larger design to examine the influence of factors on a particular variable.
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.