# Multiple Linear Regression (MLR)

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## Definition of 'Multiple Linear Regression (MLR)'

Multiple linear regression (MLR) is a statistical technique that uses several independent variables to predict a single dependent variable. It is a linear regression model with multiple independent variables.

MLR is used in a wide variety of applications, including finance, marketing, and economics. In finance, MLR can be used to predict stock prices, interest rates, and other financial variables. In marketing, MLR can be used to predict customer behavior, sales, and other marketing metrics. In economics, MLR can be used to predict economic growth, inflation, and other economic variables.

The basic idea behind MLR is that the dependent variable is a linear function of the independent variables. In other words, the dependent variable can be expressed as a linear combination of the independent variables.

The equation for a simple linear regression model is:

```
y = b0 + b1x1
```

where:

* y is the dependent variable
* b0 is the intercept
* b1 is the slope
* x1 is the independent variable

In a multiple linear regression model, there are multiple independent variables. The equation for a multiple linear regression model is:

```
y = b0 + b1x1 + b2x2 + ... + bnxn
```

where:

* y is the dependent variable
* b0 is the intercept
* b1, b2, ..., bn are the slopes
* x1, x2, ..., xn are the independent variables

MLR can be used to solve a variety of problems. For example, MLR can be used to:

* Predict the value of a house based on its size, number of bedrooms, and location.
* Predict the sales of a product based on its price, advertising, and distribution.
* Predict the growth of a company based on its revenue, expenses, and assets.

MLR is a powerful tool that can be used to solve a variety of problems. However, it is important to note that MLR is not always the best solution. In some cases, other statistical techniques may be more appropriate.

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