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Regression Prediction Calculator

Enter the intercept, slope and x to predict y.

How it is calculated

y = a + b × x, where a is the intercept (the predicted y when x is 0), b is the slope (the change in y for each one-unit increase in x) and x is the new value of the predictor. The tool does not fit a line to data: you must bring a and b from your own regression output. The result is rounded to two decimals and can be negative. Units are whatever your data used: y comes out in the units of y, and the slope must be per one unit of x as measured in the model. Nothing here depends on country or year. The value is a point estimate only; it has no confidence or prediction interval.

Worked example

A fitted model predicts an exam score from hours studied, with intercept 12.5 and slope 2.4 points per hour. For 30 hours: b × x = 2.4 × 30 = 72. Add the intercept: 12.5 + 72 = 84.50. The predicted score is 84.50. With a slope of −2.4 instead, the same inputs would give −59.50.

Try this example →

Common mistakes

  • Swapping the intercept and the slope when copying from software output; the intercept is the constant term, the slope is the coefficient on x.
  • Using a slope from a model where x was scaled (hours versus minutes, thousands versus single dollars) and then typing x in the other scale.
  • Predicting for an x far outside the data used to fit the line. The straight-line relationship may not hold there, even though the tool returns a number.
  • Entering a coefficient from a standardized regression (beta weights) and a raw x; standardized coefficients need z-scored inputs.
  • Reading the result as exact. It is the average expected y at that x, and individual observations scatter around it.

About this calculator

Once a simple linear regression has been fitted to a dataset, giving an intercept a and slope b, the fitted line y = a + b·x can be used to predict the expected response for any new value of x — this calculator performs that final prediction step given the fitted parameters and a chosen x.

Analysts, students and researchers across economics, biology, engineering and business use this kind of point prediction after regression analysis to forecast an outcome (sales, a lab measurement, a test score) from a single predictor, though it's most reliable for x-values inside the range the model was actually fitted on, since extrapolating far beyond the data risks error.

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