Choose from several analyses and get in-depth results
Correlation is useful for trying to identify the different strengths of association that may exist between two or more variables. After running correlation, you will get a correlation matrix with values between -1 and 1. Each individual value indicates the strength and direction of the associated variables.
Linear Regression is commonly used to create a predictive model based on a given dataset. Another common use is to quantify the strength of a relationship between a dependent variable and an independent variable(s).
Logistic and Ordered Logistic Regression are similar to linear regression, but are ideal in situations in which your dependent variable is binary or ordinal. Results of these analyses will give you the ability to predict the probability of your outcome variables.
K-Means Clustering is useful when you are trying to separate your dataset into groups that are not explicitly labeled. This process can be used to validate business assumptions about the different types of groups that exist, or it can be used to identify unknown groups in complex datasets.
Factor Analysis can be used to condense a large number of variables into a smaller set of factors while still retaining core information. This method is typically used in situations where the large number of input variables is too difficult to analyze.