Linear Regression

Central to predictive analytics is the method of linear regression. The primary aim of regression is to address two elements: Can one predict an outcome variable utilizing a collection of predictor variables? Secondly, what factors significantly contribute to the prediction of the outcome variable, and what's the extent of their influence on it? To illustrate how a dependent variable interacts with single or multiple independent variables, regression estimations are exploited.

Essentially, the regression model with a single dependent and independent variable can be represented as Y = c + b*x. Here, y denotes the estimated score of the dependent variable while c is a constant. The regression coefficient and the score of the independent variable are represented by b and x, respectively.

Considered a statistical tool, linear regression analysis allows for the prediction of one variable's value based on that of another. The variable for which you want to make a prediction is the dependent variable, and the variable you use for this prediction is the independent variable.

The dependent variable in regression also goes by other terminologies: a criteria variable, an endogenous variable, or a regressand. Independent variables, alternatively, are also known as exogenous variables, predictor variables, or regressors.

The identification of a linear correlation between an objective and several variables is achieved through linear regression. The two main types of this method are simple and multiple linear regressions.

To recognize a correlation between two continuous variables, simple linear regression is employed, with one being the predictor or independent variable and the other being the response or dependent variable. This kind of regression looks for statistical relationships rather than deterministic ones — where one variable is precisely determined by another. For instance, the temperature in Fahrenheit can be precisely predicted using degrees Celsius.

Importance of linear regression

It offers an essential mathematical method to derive predictions, that can be applied in numerous professional and educational contexts.

Linear models have proven their effectiveness in biological, behavioral, environmental, social sciences, and business sectors. They are well-known for their solid scientific ability to predict possibilities and are able to be trained quickly due to their inherent known features.


There are several key applications of regression analysis. They include understanding the power of predictors, predicting impacts, and forecasting trends as well as future values. The technique allows us to evaluate how the dependent variable may change with the manipulation of one or more independent variables.

When selecting a model for analysis, model fitting is an important consideration. Adding independent variables to a linear model will always increase the explained variance. However, adding too many variables leads to overfitting, compromising the model's generalizability.

Business and organizational decision-making can be improved through the application of linear regression techniques. As organizations have access to large amounts of data, the use of linear regression results in more informed decisions, enabling a better understanding of reality.

Also, with linear regression providing better insights and revealing patterns and relationships, businesses can effectively utilize vast amounts of unstructured data for their benefit. For instance, analyzing sales and buying data can disclose specific buying behaviors at certain times or days.

With regression analysis, business leaders can gain insights on when their products will be in peak demand, leading to more efficient operations and increased profitability.

Integrate | Scan | Test | Automate

Detect hidden vulnerabilities in ML models, from tabular to LLMs, before moving to production.