Statistical modeling is a powerful tool used in various fields such as economics, medicine, psychology, and more to make predictions and uncover relationships between variables. One commonly used statistical modeling technique is logistic regression, a type of regression analysis used when the dependent variable is binary.
Survival analysis models are a powerful tool used in statistical modeling to analyze the time until an event of interest occurs. This type of modeling is commonly used in medical research, epidemiology, and financial forecasting, among other fields.
Regression models are a powerful statistical tool used to understand the relationship between variables and make predictions based on that relationship. In simple terms, regression analysis helps us to predict an outcome based on input variables. It is one of the most commonly used statistical techniques in various fields such as economics, finance, marketing, and social sciences.
Canonical Correlation Analysis (CCA) is a powerful multivariate analysis technique that allows researchers to explore the relationships between two sets of variables. By identifying the underlying correlations between these sets of variables, CCA can reveal valuable insights into the underlying structure of data and help draw meaningful conclusions.
Cluster analysis is a powerful multivariate analysis technique that is widely used in various fields such as biology, marketing, and social sciences. It is a method of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.
Principal Component Analysis (PCA) is a powerful tool in multivariate analysis that is widely used for dimensionality reduction and data visualization. In this blog post, we will explore what PCA is, how it works, and its applications in various fields.
Non-parametric statistics play a crucial role in data analysis when certain assumptions of parametric statistics are not met. One common type of non-parametric test is the rank correlation test, which is used to assess the relationship between two variables based on the ranks of their values rather than the actual values themselves.
Non-parametric statistics are a powerful tool in the field of data analysis, particularly when dealing with non-normal or skewed data. One commonly used non-parametric test is the Sign Test, which is useful for comparing two related or matched samples without making any assumptions about the shape of the data distribution.