Multivariate Analysis

Multivariate analysis seeks to study numerous parameters and then to see their connections and consequences.

Factor Analysis

Identifying Underlying Relationships: Conversion of data volume into latent arrays of variables that explain the observed correlations between different variables.

Principal Component Analysis (PCA)

Reducing Dimensionality: PCA Transforms data into principal components by using them in the first tower to show the peak of variations and simplifying the data set while retaining most of the information.

Cluster Analysis

K-means Clustering: Partition data into k clusters sharing similarity of content by minimizing the variance internal to the clusters.
Hierarchical Clustering: Hierarchical structure accomplished by creating a sequence based on distance metric values to form a tree-like structure.

Bayesian Analysis

Bayesian analysis like the prior information is already given during the modeling process.
MCMC (Markov Chain Monte Carlo) represents one of the most crucial steps in mixture clustering algorithms.
Sampling Methods: Techniques like Metropolis-Hastings algorithm and Gibbs sampling are used in order to get the posterior distribution, if it is hard or impossible to compute directly.

Types of Statistical Data Analysis

Statistics data analysis is a class of analysis that includes different techniques and methods for collection, data analysis, interpretation and presentation of data. Knowing the approach to data analysis is one of the crucial aspects that allows drawing a meaningful conclusion. In this article, the most fundamental types of statistical data analysis will be described. The authors will explain all the terms and concepts easily.