What Is Regression Analysis and How Data Analysts Use It

Regression analysis is a set of statistical methods that estimate how one outcome changes when one or more other variables change. An analyst might use it to answer questions like “How much do sales rise for every extra dollar of ad spend?” or “Which factors best explain why customers cancel?” The outcome is called the dependent variable, and the factors that may explain it are the independent variables (or predictors).

Why it matters now: dashboards tell you what happened, but stakeholders increasingly ask why and what if. Regression is the most common bridge between descriptive reporting and real explanation or forecasting, and it is the foundation for many machine learning models. Knowing how to run it, read it, and explain its limits is one of the clearest signs of a strong data analyst.

How regression works, in plain language

Picture a scatter plot of monthly ad spend (x-axis) against monthly sales (y-axis). Simple linear regression draws the single straight line that fits those points best, usually by minimizing the sum of squared distances between each point and the line (a method called ordinary least squares, or OLS). That line has two numbers you care about:

• Intercept: the expected value of the outcome when the predictor is zero.

• Slope (coefficient): how much the outcome changes, on average, for a one-unit increase in the predictor.

If the slope is 4.2, the model estimates that each extra dollar of ad spend is associated with about $4.20 more in sales, on average, within the range of data you observed. Harvard Business Review’s refresher on regression analysis walks through this same intuition for business readers.

The main types of regression analysts use

Simple linear regression

One predictor, one continuous outcome. Useful for quick, explainable relationships, like price versus units sold.

Multiple linear regression

Several predictors at once, such as ad spend, season, and discount rate. Each coefficient is interpreted “holding the other variables constant,” which helps separate overlapping effects.

Logistic regression

Used when the outcome is a yes/no category, like churned versus stayed or converted versus not. Instead of predicting a number, it predicts a probability. It is one of the most widely used classification models in analytics and machine learning.

Polynomial and regularized regression

Polynomial terms let the line curve when the relationship is not straight. Regularized versions like Ridge and Lasso shrink coefficients to reduce overfitting when you have many predictors. The scikit-learn linear models guide documents all of these variants.

How to read a regression output

Tools like Excel, R, or Python’s statsmodels produce a summary table. These are the numbers to check first:

• Coefficients: the direction (positive or negative) and size of each predictor’s effect.

• p-values: whether a coefficient is distinguishable from zero given the noise in your data. A small p-value does not mean the effect is large or important.

• Confidence intervals: the plausible range for each coefficient. Wide intervals mean you should be cautious.

• R-squared: the share of variation in the outcome the model explains. A low R-squared can still be useful; a high one does not guarantee the model is right.

• Residuals: the gaps between predicted and actual values. Plot them; patterns mean the model is missing something.

If those terms feel rusty, our guide to statistics for data analysts covers the essentials in learning order.

A simple workflow for your first regression

1. Start with a business question. “What drives weekly signups?” is better than “let’s regress everything.”

2. Explore the data. Plot each predictor against the outcome, and check for missing values and outliers that can pull the line.

3. Fit a simple model first. Add predictors one at a time and watch how coefficients change.

4. Check assumptions. Look for a roughly linear relationship, residuals without obvious patterns, and predictors that are not near-duplicates of each other (multicollinearity).

5. Validate. Hold out part of the data and see whether the model predicts it reasonably well.

6. Explain it in business terms. Translate coefficients into sentences a manager can act on, and state the limits clearly.

Common mistakes to avoid

• Reading coefficients as causes. Regression on observational data shows association, not proof. Read correlation vs. causation before you present “drivers” to leadership.

• Extrapolating too far. A model fitted on spend between $1k and $10k says little about $100k.

• Ignoring omitted variables. If seasonality drives both ad spend and sales, leaving it out inflates the ad coefficient.

• Chasing R-squared. Adding predictors always raises it on training data, even when they add noise.

Frequently asked questions

Is regression analysis the same as machine learning?

Linear and logistic regression are among the simplest machine learning models. The difference is often the goal: analysts tend to use regression to explain relationships, while machine learning focuses on predictive accuracy on new data.

Can I do regression analysis in Excel?

Yes. Excel’s Analysis ToolPak and the LINEST function handle linear regression. For larger datasets, diagnostics, and logistic models, Python or R are more flexible.

How much data do I need?

There is no single rule, but more predictors need more rows. A common rough guideline is at least 10 to 20 observations per predictor, and you should always validate on data the model has not seen.

Keep learning

If you want to practice regression on real business datasets and later apply the same ideas to AI models, explore these programs:

• Data Analytics Course

• AI Engineering Course