Why should we include more than one variable in our regression?
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Why should we include more than one variable in our regression?
If a variable to be studied depends upon a single variable then this can be studied by simple regression model.so only we can study about one variable depends on the other variable there is no any other factors depends on the other variable. from one point of view, is an attempt to fill some research gaps.
But if a variable to be studied depends upon more than one variables then this can not be studied by simple regression model. This study is possible only by multiple regression model.
In reality, any variable to be studied usually depends upon many variables though it is is a usual practice to treat the variable to be dependent on a single variable.
Accordingly, multiple regression model is to be considered in order to obtain findings with greater accuracy.
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