Regression Confession
Explore how to use linear and logistic regression to analyze data effectively. This lesson helps you understand how to test relationships, quantify variable effects, and make predictions based on statistical evidence. You will learn to interpret regression outputs and apply these methods to real-world scenarios for better data-driven decisions.
We'll cover the following...
As data analysts, we often try to make sense of patterns in data and explain them in a way others can understand. People will ask questions like: Does income affect purchases? Does battery size predict phone longevity?
We need a reliable method to move beyond simply describing trends and actually test relationships. This is where a regression test comes in.
What is a regression test?
A regression test is a statistical approach that helps us:
Model relationships between variables.
Measure the strength and direction of those relationships.
Test hypotheses about cause and effect.
Predict future outcomes based on past data.
Instead of relying on assumptions or intuition, regression lets us test claims like: “Does income significantly influence purchase decisions?”
Types of regression tests
Different regression tests are suited for different kinds of outcomes. The test we choose depends on the type of variable we’re trying to predict.
Regression Type | Description | Target Variable | Common Use Cases |
Linear regression | Tests linear relationships with numeric outcomes | Continuous (e.g., price) | Predicting sales, income, weights |
Logistic regression | Tests probability of binary outcomes | Binary (e.g., 0/1) | Churn prediction, marketing conversion |
Multiple regression | Tests multiple inputs at once | Continuous | Controlling for multiple factors |
Poisson regression | Tests relationships with count-based targets | Count (e.g., clicks) | Website visits, event occurrences |
Ordinal regression | Tests effects on ranked outcomes | Ordered categories | Customer satisfaction (low, medium, high) |
In this lesson, we’ll focus on the two most useful regression tests for data analysts: linear regression and logistic regression.
Linear regression
Linear regression is one of the most useful tools when we’re working with numeric outcomes. It’s especially helpful when the variable we want to predict is continuous, like price, revenue, or performance score. It also helps us understand how much each factor contributes to the result.
As analysts, we’re often tasked with more than just reporting values. We’re expected to explain why something happened and what might happen next. That’s where regression tests shine. They let us model relationships between dependent and independent ...