# “Multiple Regression” strategy to deploy in your Phd Study

You would know about linear regression. For the uninitiated, it is the simplest and most largely used statistical model for predictive modeling. Well, mathematicians generally prefer to describe values as either dependent or independent variables. Independent variables are the specific inputs of the experiment and dependent variables are the output. The dependent variables rely on the outcome of the experiment. The target variables rely on the independent variables. Difference between Linear Regression and Multiple Regression analysis in Research

Multiple regression is quite an extension of simple linear regression. It is applied when we want to estimate the value of a variable dependent on the value of two or more other variables.

Now we will see in what way multiple regression is an extension of linear regression. While in the linear regression, we have to apply only one independent variable and dependent variable, in multiple regression we have to use more than one independent variable and one dependent variable. However, in both regression analyses, the key point is the dependent variable and we have to predict the value of it. This is done based on the value of independent variable.

Example of multiple regression

The exam grades of a student will depend on several aspects including concentration in the class, the knowledge of the lecturer, the amount of sleep he/she gets before the exam etc. Using the multiple regression, one can predict the correct relationship among these factors.

The values used for multiple regression and their application

• R-Squared denotes the variable that is discussed in the model. It is the extent to which your input variables talk about the variation of your output.

• ANOVA table is used in order to compare mean differences between 2 or more groups. It performs this action by looking at variations in the data and the place of variation.

• ANOVA computes a statistics test (the F-ratio). F-ratio explains whether the entire regression model is a good fit or not for the data.

• Predicted model coefficients table comprises the following predictors: t-value and p-value. These values help to know whether each variable is substantially estimating the dependent variable.

• Beta (β) coefficients are the estimated coefficients of independent variables. This table also contains the interval estimator of independent variable.

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