No, a line of best fit does not have to go through the origin. By default, the standard method for calculating a line of best fit (least squares regression) finds both a slope and a y-intercept that together minimize the overall distance between the line and your data points. That intercept can land anywhere, and it only equals zero if the data itself happens to produce that result.
There is, however, a special technique called “regression through the origin” where you deliberately force the line to pass through (0, 0). Whether that’s appropriate depends entirely on the situation, and getting it wrong can throw off your results.
How the Intercept Is Normally Calculated
When you fit a standard line of best fit, the math solves for two values: a slope and an intercept. The intercept equals the mean of all your y-values minus the slope multiplied by the mean of all your x-values. In other words, the line always passes through the point where both averages meet. Unless the data naturally centers in a way that places that point on the origin, the intercept will be some nonzero number.
Think of it this way: the algorithm doesn’t “know” that zero is special. It treats (0, 0) the same as any other point on the graph. If your data clusters far from zero, the best-fitting line may not come anywhere near the origin, and that’s perfectly normal.
When You Would Force the Line Through the Origin
Sometimes theory or logic tells you that when x is zero, y must also be zero. In those cases, you can force the intercept to zero before calculating the slope. This is regression through the origin, and it’s used in specific disciplines where the relationship demands it.
A classic example comes from physics. Hooke’s Law says the force on a spring is proportional to how far the spring stretches: force equals a constant times displacement. If there’s no displacement, there’s no force, so the relationship must pass through the origin. In a lab at BYU, students measured spring displacement and force at seven data points, then fit a line through the origin to find the spring constant (about 2.9957). Adding a y-intercept here would make no physical sense, because a spring with zero stretch cannot exert force.
Other situations where forcing through the origin makes sense include converting between units (zero in one unit is zero in the other), or any proportional relationship where the absence of one quantity guarantees the absence of the other.
When You Should Not Force It
If there’s no strong theoretical reason to force the line through zero, don’t. The consequences can be significant.
UCLA’s statistics department illustrates this with reading and writing test scores. When they ran a standard regression, the intercept came out to about 24, meaning a student who scored zero on reading would still be predicted to score 24 on writing. That nonzero intercept reflects reality: writing ability doesn’t vanish just because someone bombs a reading test. Forcing the line through the origin here would distort the slope and produce worse predictions across the entire range of scores.
When you force a zero intercept on data that naturally has a nonzero one, the line pivots around the origin to find the best fit under that constraint. According to guidance from the Arizona Department of Health Services on lab calibration, this biases the fit toward the low end of your data range, pulling the line away from where it should be at higher values. You also lose a degree of freedom in your calculation, which makes your statistical estimates slightly less reliable.
How to Decide
Most statisticians recommend a straightforward approach: run the regression with the intercept included, then check whether the intercept is statistically different from zero. If testing shows the intercept is essentially zero and you have a theoretical reason to expect it, you can justify removing it. If the intercept is clearly nonzero, forcing it to zero will hurt your model’s accuracy.
The key question to ask yourself is whether zero input truly guarantees zero output. If you’re measuring something like the relationship between hours studied and exam score, zero hours of studying doesn’t produce a score of zero (people guess, have prior knowledge, etc.). Forcing the line through the origin in that case would be a mistake.
How to Do It in Spreadsheets
In Excel, you can force a trendline through the origin by right-clicking the trendline, selecting “Format Trendline,” and checking “Set Intercept” to 0. Google Sheets doesn’t offer a built-in option for this on chart trendlines. The workaround is to use the LINEST function to calculate your slope manually with a zero intercept, then plot those predicted values as a separate series on your chart.
By default, both programs calculate a trendline with a free intercept, which is the safer choice for most situations. Only override this when you have a clear reason to do so.