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Regression Analysis in Data Analytic

Regression Analysis

In the domain of statistics, the most commonly used statistical technique is Regression Analysis which is used to estimate particular relationships among variables. Under this technique, the main focus is upon the relationship between dependent variable and any one or more independent variables. There are several techniques within this analysis that are used for modeling and analyzing several variables. This technique helps you see how the particular value of a dependent variable changes when any one of the independent variable varies with all others fixed. In simple terms, through this approach you get to estimate the conditional expectation or the average value of the dependent variable. Thoroughly in all the cases, the target for estimation is a function of any one or more independent variables, which is termed as regression function. The main goal of regression analysis is to ascertain the values of all the parameters to derive a function that will fit the data observations in the best way possible.

Common uses of Regression Analysis

Most widely, this technique is used for predicting and forecasting. In both these fields, the uses overlap with that of the domain of machine learning. The technique also helps you in figuring out the form and type of relationships that forms between a dependent variable and the independent variable. It also interprets the casual relationships between the same.

There are a variety of techniques within data analytic that are employed to carry out regression analysis. Some of the famous ones are- Linear Regression Analytic, Logistic Regression Analytic and ordinary least squares. Linear regression and squares techniques are parametric; in both of these methodologies regression function is managed from the limited number of peculiar parameters.

The way how regression analysis methods perform depends on the types of the data generating process. Below given is the list of variables that various regression models incorporate

• The dependent variable, Y
• The independent variable, X
• The unknown parameters, β. It is represented as a scalar or a vector

Data Analysis

Data Analysis can be categorized into the following modes

• Narrative. For example, laws and arts.
• Descriptive. For example, fields of social sciences
• Statistical and Mathematical. For example, both pure and applied sciences
• Audio-Optical. For example, varied domains of telecommunication

Data Analytic Techniques

There are innumerable data analytic techniques that are used to manage the enormous data. Find enlisted the not so commonly used analytic techniques:

• Cluster Analysis
• Experimental Simulation
• MTMM
• Conjoint Analysis
• Multi-normal distribution
• Correspondence analysis
• Factor analysis
• SEM (Structural Equation Modeling)
• Linear probability models
• R-squared or R2: Coefficient of determination
• LISREL
• Multiple discriminant analysis
• PCA (Principal Components Analysis)

Conclusion

Of all the above mentioned analytical techniques, regression techniques are the most sought after data analytical methodologies. Not only in biomedical domain, this technique is also employed by data scientists in banking, insurance, retail, pharmacy, e-commerce and varied other fields. Mostly the linear and logistic regressions are induced into the data inventory by those companies which indulge in generating a large amount of data. Nowadays data analytic is more commonly known as regression analysis. As the world is gaining pace onto the digital world, more and more corporates are now opting for regression analysis outsourcing. This outsourcing helps them in gaining super excelled data scientists and sophisticated data storage centers.