What are Regression Analysis and Why Should we Use this in data research?
Introduction
Regression analysis is widely utilised with SPSS help for data analysis and interpretation in order to in statistical modelling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. Through this article, it is possible to understand regression analysis and its importance in data research.
Regression
The formula of regression is,
For linear regression analysis, it is,
Y = a + bX
Where X is the explanatory variable
Y is the dependent variable
b is the slope of the line
a is the y-intercept
For regression analysis, the formula is,
Y = f (X, ß) + e
Where,
Y is the dependent variable
X is the independent variable
f is the function
ß is unknown parameter
e is the error term
These are the main formula, through which the researchers can interpret the data findings for further discussion. Through the regression analysis, it is possible to identify the correlation analysis between the decedent and independent variables as well as consider the error in case of any errors in the data. The major types of regression are such as linear, logistics, polynomial, Bayesian linear model, lasso and ridge regression analysis. Correlation quantifies the strength of the linear relationship between a pair of variables in the data set, whereas regression expresses the relationship in the form of an equation. Hence, the above-mentioned equations are utilised for analysing regression and exploring the inter relationship between two variables in the data set. the above-mentioned equation indicates that the dependent variable will be changing with different values of the independent variables and through statistical analysis, the researchers can identify the interlink between the variables.
The sign of regression coefficient indicates that, whether there is a positive or negative correlation between each independent variable and the dependent variable in the data set. A positive coefficient states that, as the value of the independent variable increases, the mean of the dependent variable also tends to increase. Like this, if the sign of the coefficient is negative, it further indicates that there is a negative relationship between the independent and dependent variable, such as if the independent variable tends to increase, the dependent variable will tend to decrease.
