State-of-the-art Regression In Minutes

Q: How do I perform regression analysis in Excel?

To perform regression analysis in Excel:Go to Data > Data Analysis (enable the Analysis ToolPak if needed).Select Regression and choose your input data range.Define the dependent (Y) and independent (X) variables.Click OK to generate regression output, including coefficients, R-squared values, and significance tests.

Harness the power of regression analysis with Displayr. Our intuitive tools make it easy to uncover key data relationships, empowering confident, data-driven decisions and insightful trend analysis.

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Unlock insights with advanced regression tools

Easy to use

Drag and drop data to run a regression (no need to write code). The outputs automatically show the key results in an easy-to-digest format, so you don’t need to spend time working out which output to read and why.

Expert systems that make you an expert

Displayr’s expert systems guide you through choosing the appropriate type of regression – linear, logistic, ordered logit, NBD, etc. – and automatically solving many of the uglies of regression analysis, such as outliers, multicollinearity, heteroscedasticity, and categorical outcome variables.

Designed for survey data

If you have ever read their manuals, you will know that most regression software is not designed for survey data, and will calculate statistical significance incorrect if you use weights. Displayr uses Taylor Series Linearization to appropriately perform the regression with weighted data.

Best-in-class for missing data

One of the great innovations in regression in the past few years has been the use of multiple imputation for missing data. Displayr automates this entirely. And, you can still use the older methods if you prefer them as well.

Automated updating

Analysis will automatically update when you revise the data, whether filtering, data cleaning, or adding in a new wave of data.

Life without Displayr would be like going back to the dark ages. I don’t even want to imagine it.

Michael Alborough

Founder, MAC Research

10x faster regression

One complete platform to do it all

Automatic theme detection

AI automatically identifies and categorizes themes within your text data, providing deeper insights.

Emotion detection

Understand and analyze complex emotions like frustration and sadness, helping you understand customer motives.

Entity extraction

Extract key entities like names, places, and organizations to enrich your analysis.

Customizable categories

Fine-tune and adjust categories to match your specific needs and preferences.

Text visualization

Create stunning word clouds, charts, and dashboards that help tell the story behind your text.

Global language support

Analyze text data in any language, with true native language support to a global audience.

Sentiment analysis

Analyze large volumes of text to gauge positive, negative, or neutral sentiments.

Natural Language Processing

Extract insights with unrivalled accuracy, utilizing NLP to reduce manual effort and free up time.

Case Study - CYGNAL

Global Survey Research Company

Displayr helps Cygnal cut report creation time in half

Challenges

Time-consuming manual processes
A need to offer dynamic data presentations to stay competitive—and no time to create them

Solutions

An all-in-one statistical package built for survey data with presentation and dashboarding tools

Results

50% time saved on data presentations and reports
More deals closed

“Displayr is at least 50% faster than just creating a PowerPoint presentation. In some cases, I think it’s even higher than 50%.”

Matt Hubbard

VP Data & Analytics, Cygnal

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Regression FAQs

What is regression, and how does it work?

Regression quantifies the relationship between an outcome variable and one or more predictor variable. This might be looking at a company’s how sales would be impacted by a pricing change. There are several different types of regression – each serving a different purpose – including linear, logistic, ordered logit, NBD, and more. Linear regression is the most commonly used model.

How do you select the right regression?

Estimating regression is relatively simple. The tricky part can often be selecting the correct regression model for your data. The right regression model depends on your data type, variable relationships, and analysis goals, whether for prediction, explanation, or pattern detection.

How is regression used to predict sales?

Businesses use regression analysis to forecast sales by identifying relationships between sales data and influencing factors such as pricing, marketing spend, seasonality, and customer behavior. This insights helps optimize strategies and improve revenue projections.

How do you do regression in Displayr?

Displayr allows users to create regression models with just a few clicks. Various models are available via Anything > Advanced Analysis > Regression. You can also check out this guide on how to run linear regression in Displayr.

How do I perform regression analysis in Excel?

To perform regression analysis in Excel:

Go to Data > Data Analysis (enable the Analysis ToolPak if needed).
Select Regression and choose your input data range.
Define the dependent (Y) and independent (X) variables.
Click OK to generate regression output, including coefficients, R-squared values, and significance tests.

What are the best tools for regression analysis?

Several software tools support regression analysis, including:

Displayr (no-code workflows for quick analysis)
R (advanced statistical computing)
Python (Statsmodels, scikit-learn)
Excel (basic regression tools)
SPSS, SAS, and Stata (industry-standard statistical software)

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