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# Learn how linear regression is applied and what its characteristics are

Linear regression is a statistical analysis method used to predict the value of a dependent variable (Y) in relation to one or more independent variables (X). In this method, a straight line is used to fit the data in order to predict the value of Y based on X.

It is used to estimate relationships between variables and predict future outcomes from historical data.

In this content you will see how linear regression is applied, what are its characteristics and much more. Keep reading to check it out!

## What is linear regression?

Linear regression is a data analysis technique used to predict values ​​of variables of interest. It is an important statistical method applied to estimate the magnitude of a relationship between variables. Linear regression is used to predict results from input data and is generally used to predict future results.

The objective of linear regression is to estimate the relationship between a dependent variable (Y), which is the response you want to predict, and one or more independent variables (X), which represent the factors that affect the result. From this relationship, an equation is created that can be used to predict the outcome of Y for a given value of X.

Linear regression has several important features. First, it assumes that there is a linear relationship between the variables Y and X, which means that the rate of change of Y with respect to X is constant.

Furthermore, it also assumes that there are no hidden variables that would affect the result and that the data are uncorrelated. Finally, it assumes that there are no measurement errors and that the data are normally distributed.

## What are the main features of linear regression?

Check below what are the main characteristics of linear regression:

• Linear relationships: Linear regression assumes that there is a linear relationship between dependent and independent variables;
• Independent variables: Independent variables determine the behavior of the dependent variable;
• Regression coefficients: Linear regression calculates regression coefficients (also known as parameters) that allow you to describe the relationship between variables;
• Random errors: Linear regression assumes that errors are random and do not follow any particular distribution;
• Point estimates: Linear regression provides point estimates for the dependent variable;
• Interval Estimates: Linear regression also allows you to estimate confidence intervals for point estimates.

These characteristics are essential for the calculation to be applicable to its objective. Let’s understand when to apply linear regression next.

## What are the main uses of linear regression?

In addition to being used for stock price forecasting and sales forecasting, linear regression is also used to estimate:

• The relationship between two variables;
• The influence of one factor on another;
• The probability that an event will occur;
• The relationship between factors and outcomes;
• The value of a property;
• The relationship between investments and returns;
• The relationship between expenses and profits;
• The relationship between supply and demand.

Check below how linear regression can be applied to machine learning problems.

## How can linear regression be applied to machine learning problems?

Linear regression can be used in Machine Learning problems to predict continuous variables such as stock prices, house prices and other forecasting variables.

The linear regression algorithm can be applied to model the relationship between several exogenous variables and a response variable. It is very useful for prediction problems where there is a clear linear relationship between variables. Linear regression can also be used to identify which variables are most important for forecasting.

## What are the main limitations of linear regression?

The main limitations of linear regression include: assuming that there is a clear linear relationship between variables; being able to have problems with multicollinearity – when two or more variables have a very high correlation; not being able to handle categorical variables; not being able to deal with non-linear data; and not being sensitive to outliers – data that do not follow the general trend.

Linear regression analysis is a useful technique for understand the relationship between variables, allowing researchers to predict the behavior of one variable based on the other. For this reason, we can conclude that it is fundamental.