What is a Line of Best Fit?
A straight line drawn through a scatter plot that best represents the trend of the data. It minimises the overall distance between the line and all the data points.
When we collect data on two variables — like study hours and test scores, or temperature and ice cream sales — we often want to know: is there a pattern? And if so, can we use that pattern to make predictions?
A line of best fit (also called a regression line) is the tool we use to answer exactly that. It summarises the relationship between two variables in one clean, straight line.
Imagine you're trying to draw the "middle path" through a crowd of people walking in roughly the same direction. You wouldn't zigzag through every person — you'd draw one smooth line that captures the general direction of the crowd. That's exactly what a line of best fit does with data points on a scatter plot.
Two-Variable (Bivariate) Data
Data that involves two variables collected from the same source, allowing us to investigate whether a relationship exists between them.
In two-variable analysis, we always label our variables:
- x — the independent variable (the one we control or choose)
- y — the dependent variable (the one that responds or changes as a result)
For example, if we are studying how the number of hours studied () affects exam score (), we would plot on the horizontal axis and on the vertical axis.
Every line of best fit is only meaningful if there is actually a linear relationship between the two variables. Before drawing one, always check your scatter plot to see if the data roughly follows a straight-line pattern.
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