Abstract
Data analysis is a process of studying, removing non-required data in the view level, and converting to needed patterns for sub decisions to make an aggregated decision. Statistical modeling is the process of applying statistical techniques in data analysis for taking proactive decisions depend requirements. The statistical modeling identifies relationship between variables, and it encompasses inferential statistics for model validation. The focus of the chapter is to analyze statistical modeling techniques in different contexts to understand the mathematical representation of data. The correlation and regression are used for analyzing association between key factors of companies' activities. Especially in business, correlation describes positive and negative correlation variables for analyzing the factors of business for supporting the decision-making process. The key factors are related with independent variables and dependent variables, which create cause and effect models to predict the future outcomes.
TopBackground
Data Analytics is defined as extracting meaningful information continuously with the assistance of specialized system which facilitates modeling the data, information transformation and organization of functions for finding patterns to get feasible solution. It has become an essential process for many companies to take out solution from huge volume of regular storing data in a system for best solution in business context like Google, twitter, Amazon, yahoo etc(Davenport & Jill, 2013). The industries, collected data from different organization might be used for new business income streams and data analytics process involves functions of data collection, sorting and processing to ensure the effectiveness of the data for providing reliable result (Russom, 2013). The data analysis generates an important interest in business intelligence to assist to detect market scenario for making decision in short span of time and higher profit (Chen, Roger, & Veda, 2012). The growth of data analytics has been facing many issues like programming, non-supporting of whole business services. As the datasets volume is so huge, the tools are not able to manage and evaluate the process of operations in time within the cost limit (Jiang & Chai, 2016). The establishment of business processes using business analytics is widely accepted by most of the business industries (Wang & Zhao, 2016). As data analytics is the essential to the business development, each and every operation of business try to accommodate the system facilities in the maximum level for getting feasible solution. The higher facilities are the opportunities of business evaluation in all the ways. The adaptation of new approaches leads to evaluate the available data for best solution based on context (Sosna et al., 2010).
Key Terms in this Chapter
Bivariate Analysis: It is one of the statistical analyses where two variables are involved to figure out the depth of the relationships between variables.
Data Analytics: The science of extracting meaningful information continuously with the assistance of specialized system for finding patterns to get feasible solutions.
Regression Analysis: It is a statistical process for denoting the average relationship between two or more factors with the involvement of dependent and independent variables.
Statistical Modeling: It represents a mathematical model which encompasses statistical assumptions to interpret the available data for approximating reality.
Multivariate Analysis: It is a subdivision of statistics for analyzing three or more piece of information for every item to find the interrelationship between variables to predict the future outcomes.
Predictive Analytics: The finding of new patterns is to analyze future performance of a system based on context and historical information and it is used for decision making process.
Univariate Analysis: It is a simplest form of statistical analysis which involves with single variable for finding solution in predictive analytics.
Correlation: The degree of relationship between two or more variables and it does not reflect cause and effect relationship between the factors.