Linear Regression Calculator
Enter Your Data Points
Regression Results
Regression Equation
The formula that best fits your data
Slope (b)
Change in y per unit change in x
Intercept (a)
Expected y value when x = 0
R² (Coefficient of Determination)
Goodness of fit (0 to 1)
Correlation Coefficient (r)
Strength and direction of relationship
Prediction for x =
Regression Visualization
Frequently Asked Questions About Linear Regression
What’s the difference between correlation and regression?
Correlation measures the strength and direction of a relationship between two variables. Regression goes further to create an equation that predicts one variable from another. While correlation tells you if a relationship exists, regression helps you quantify and use that relationship for predictions.
How many data points do I need for reliable linear regression?
For basic analysis, you need at least two points (which will always create a perfect line). For meaningful results, aim for 10-15 observations minimum. For publication-quality research, many fields require 30+ data points. More data generally improves reliability, but quality matters more than quantity alone.
Can I use linear regression for time series data?
Yes, but with caution. When time is your X variable, you’re performing time series regression. This is common in economics, climate science, and business forecasting. Remember that time-based data often has trends, seasonality, and autocorrelation that basic linear regression may not fully capture.
What does an R² value of 0.75 mean?
An R² of 0.75 means 75% of the variation in your Y variable can be explained by its relationship with X. This is generally considered a strong relationship in most social sciences and many business applications. The remaining 25% of variation comes from other factors not in your model or random variation.
Why is my regression line different when I add more data points?
Linear regression finds the line that best fits ALL your data points. Adding new points changes what “best fit” means. This is normal and reflects the model adjusting to new information. If adding a few points dramatically changes your line, check if they might be outliers or errors.
Can I use this calculator for multiple regression?
Our current calculator handles simple linear regression (one X, one Y). For multiple regression (several X variables predicting one Y), you’d need specialized software. However, for many real-world situations, simple linear regression provides valuable insights and is easier to interpret and explain to stakeholders.