## What is a Multiple Linear Regression?

Multiple linear regression is a statistical technique in which we have one dependent and several independent variables. Suppose the problem we are looking at can be treated as a problem of one dependent and several independent variables. In that case, it is suitable to use the multiple regression method for data analysis.

**Where is linear regression usually used?**

**predictive analysis and modeling**. For example, it can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable).

### An Example Of Multiple Regression Analysis

For example, a researcher wants to examine whether stress level and anxiety level predict test scores. In that case, we have one dependent variable – the exam result, and two independent variables – stress level and anxiety level.

**What are the use of null and alternative hypothesis for the Simple Linear Regression?**

Therefore, we test the following hypotheses:

* Null hypothesis: *The stress level and anxiety level do not significantly predict exam scores.

** Alternative hypothesis:** The stress level and anxiety level significantly predict exam scores.

## R function to Compute Multiple Linear Regression

The code to run a Linear Regression using R is as follows:

lm (DV~ IV1 +IV2,+…+IVx data = dataframe)

**DV**: dependent variable

**IV**: Independent variables