# What is Regression?

Regression is one of the most critical components of machine learning, so it’s a good idea to familiarize yourself with this relatively simple idea now — before you learn about the slightly more brain-melting concepts like gradient descent, hyperparameters, and reinforcement learning! (Don’t worry, enrolling in a data science program will help you master all of these!)

Let us give you a comprehensive answer to the question, “What is regression?” below and explain the benefits of The Data Incubator’s programs for future data scientists like yourself.

## What is the Meaning of Regression in Machine Learning?

The simplest definition of regression is:

Regression, or regression analysis, is a type of statistical method that lets you investigate the relationship between a dependent variable and independent variables. It helps you understand whether changes in a dependent variable are associated with changes in one or more independent variables.

Let’s give you an example:

Say you want to find out what factors influence the price of a house. Here, the dependent variable is house price. Regression lets you know whether any changes in this variable are associated with changes in independent variables, such as market conditions or time of the year. For example, the price of a house (dependent variable) might decrease because of a recession (independent variable).

Regression is commonly used for predictive modeling in machine learning, helping you predict future outcomes from modern data sets. However, the term “regression” dates back to the 1880s, when polymath Francis Galton investigated the regression relationship between the height of fathers and their sons in a famous paper.

Want to get an even more in-depth answer to “What is regression?” Learn this concept in one of The Data Incubator’s programs, such as our Data Science Essentials program! You’ll master the fundamentals of data science in as little as eight weeks part-time.

## The Benefits of Regression

The benefits of regression include understanding how variables impact one another and making better data-driven decisions. It’s also relatively easy to perform regression analysis. Let’s explore these advantages in more detail:

### Discover How Variables Influence Each Other

As a data scientist, you’ll want to find out the relationship between dependent and independent variables for more effective data analysis. Regression analysis lets you do that. For instance, regression determines whether changes to a dependent variable like product revenue is influenced by an independent variable like a customer’s age.

### Your Company Can Make Better Data-Driven Decisions

By using regression analysis, you can learn more about variables that influence business outcomes for the company you work for and share these insights with stakeholders. As a result, your organization will be able to make smarter data-driven decisions, such as hiring new staff based on predicted financial performance.

### Regression Analysis is Easy to Perform

As well as being a powerful statistical method, regression analysis has a simple learning curve. It’s all about understanding how variables influence each other, and there are no complicated calculations involved in this process if you use digital tools to perform analysis, such as Tableau and SAS Visual Analytics.

Regression analysis is just one statistical method you’ll learn at The Data Incubator. Consider enrolling in our Data Science Bootcamp to widen your data science skills and increase your chances of finding a job in this lucrative field!

## Downsides of Regression Analysis

There aren’t many negatives of regression analysis, but you should be aware of the challenges you might face as a data scientist. Here are the two main ones:

• Regression analysis won’t work if the input data you use contains errors. You’ll need to remove any missing values, duplicated data, outliers or inconsistent data from your analysis for the best results.
• Regression is susceptible to a concept called overfitting. This happens when a regression analysis model works perfectly on training data but can’t generalize itself to new data. If creating regression models, you can avoid overfitting by increasing your training data sample and rigorously testing models.

## What are you waiting for?

Want to take a deep dive into the data science skills you need to become a successful data scientist? The Data Incubator has got you covered with our immersive data science bootcamp.