What Is Regression Analysis (and How Can Your Business Use It)?

What is the one thing all businesses have in common? Data. Everything that happens in any kind of business requires data. You need to learn how to get data, manage it, measure it, and analyze it properly for the benefit of your business. If you want to find data trends or predict sales based on certain variables, then regression analysis is the way to go. In this article, we will learn about the types, applications, and use cases of regression analysis.

What Is Regression Analysis?

Regression analysis is a process used to identify the variables that have an impact on another variable. It is used to access the strength between two variables. With this method, you can determine the relationship between a dependent and an independent variable. It can also be used to predict the future relationship between both variables. This analysis model is mostly used in the finance and investment sectors.

Why use regression analysis?

In businesses, this has numerous useful applications. Some of its benefits include;

• It can be used for machine learning languages that involve serial numbers. For instance; for testing automobiles, financial forecasting, time series forecasting, and weather forecasting and predictions.
• It can help you determine whether the relationship between two variables has the potential to become a business opportunity.
• It can stop you from making disastrous moves in your business. For instance, if you change a variable using this model, it will tell you the effect that this change will have on other variables. If you change the delivery speed, you will be able to see how this will affect customer satisfaction.
• The data model can help you solve regression issues in machine-language languages.

When to Use Regression Analysis

Regression analysis is mainly used to estimate a target variable based on a group of given features. For instance, it can be used to predict the price of a house based on features like the size of the house, the novelty of the building, the number of rooms and facilities in the house, etc.

Types of Regression Analysis

1. Linear regression:

This is the most basic type of regression analysis. It is used to measure the relationship between the scalar response and explanatory variables. It is used extensively in machine learning. This model is used when the variables are related linearly. The equation for this model is; Y’ = bX + A. Linear regression can be used to measure the impact of email marketing on your sales.

2. Logistic regression

This model is suitable for dependent variables that have discrete values. This means that the variables in use can only have two values; yes or no, true or false, 0 or 1, on or off, etc. A sigmoid curve is used to show the relationship between the target and the independent variables. The equation is; l = β0 +β1X1 + β2X2 This regression is mostly used in cases where there are large data sets with a chance of equal occurrence of values in target variables.

3. Lasso regression analysis

This model limits the absolute size of the regression coefficient, thus reducing the complexity of the model. One key benefit of this model is that it uses feature selection. This lets you select a set of features from the database to build your model. The equation is; N-1 ∑i=1NF (Xi, Yi, α, β)

4. Ridge regression analysis

The model works best in cases where there is a high correlation between independent variables. It also reduces the complexity of the model. By introducing a small amount of bias known as the ridge regression penalty, the model becomes less prone to overfitting. The equation is; β = (XTX + λ * I)-1XT Y

5. Polynomial regression analysis

This method is mainly used for curvilinear data. It aims to model the expected value of a dependent variable regarding the independent variable. This method involves fitting data points using a polynomial line. It is susceptible to overfitting, so businesses are expected to analyze the curve toward the end to ensure that the results that they get are accurate. The equation is; l =β0 +β0X1 +ε

How Businesses Use Regression Analysis

Regression analysis helps businesses to understand the representation of their data points and how they fit into their techniques. You can better understand how the value of the dependent variable changes based on how the other independent variables are held fixed. With these statistics, business professionals can select the most essential variables for their businesses and eliminate the less important ones. Here are some use cases of the regression analysis;

Business optimization:

Regression analysis provides data-driven decision-making. With this, the data collected can become actionable insights. Businesses can stop using guesswork and work with real and proven data, getting expected results.

Make better decisions:

To progress, businesses need to make more informed decisions and know exactly how these decisions will affect them. With regression analysis, companies can collect information from all their departments and analyze this information to help produce better outcomes.

Understand failure and predict success:

When businesses fail in a particular task, they can trace the failure using parameters from regression analysis. This will help them understand the source of the problem and prevent a repeat performance. Also, with regression analysis, businesses can predict success, learn the processes that lead to a successful outcome, and repeat them for a higher success rate in the organization.

It provides new insight:

When you study the analysis, you can uncover new patterns. You can see processes that have a higher probability of bringing success, see opportunities and grab them.

Analyze the effectiveness of marketing campaigns:

After a marketing campaign, a company should be able to know if the funds they invested gave them a sizeable ROI. Regression analysis will provide this answer and will also tell the particular channels that are bringing the highest returns.

What Industries Can Use Regression Analysis?

Almost every industry can benefit from regression analysis. Here are some of the tops;

• Finance
• Hospitality
• Pharmaceuticals
• Agriculture
• Sports

In conclusion, using regression analysis gives you better-informed decisions, guides your resource allocation, reduces risks, and increases profits.

How Yurbi Can Help

While Yurbi does have the ability to define custom and complex formulas that can be used for regression analysis, it’s not something we would ever pitch as a feature of Yurbi. Yurbi’s strength is in its ability to:

• securely provide your business team with ad-hoc/self-service reporting
• provide your large audience of stakeholders with interactive dashboards
• data visualization
• embedded analytics
• and scheduling and notification services.

Related to Regression Analysis, Yurbi can pull the necessary datasets needed by your data analysts to perform their tasks. Yurbi can also take the results of their efforts and communicate that information securely to those who need to know (in visual and interactive methods). Additionally, Yurbi comes with reasonable price points for small and medium-sized enterprises so that any size organization can benefit from the power of powerful business intelligence, saving you money from tools, so you can use that for more important things, like your data analysts that can focus on regression analysis. Learn more of how Yurbi can fit into your BI toolkit. Reach out to us by booking a meeting or taking advantage of the free live demo sessions we offer. Better yet, avail yourself of our free trials! We’ll be waiting!