How do you interpret the slope of the line of best fit?

The slope of the line of best fit represents the rate of change between the two variables.

When you plot data points on a graph and draw a line of best fit, the slope of this line tells you how much one variable changes on average when the other variable changes. For example, if you are looking at the relationship between hours studied and exam scores, the slope will show how much the exam score is expected to increase for each additional hour of study.

To calculate the slope, you take two points on the line of best fit and use the formula: (change in y) / (change in x). This gives you a numerical value that indicates the steepness of the line. A positive slope means that as one variable increases, the other also increases. Conversely, a negative slope means that as one variable increases, the other decreases.

Understanding the slope is crucial because it helps you make predictions. If the slope is steep, even a small change in the x-variable will result in a large change in the y-variable. If the slope is shallow, the y-variable changes only slightly with changes in the x-variable. This can be particularly useful in real-life situations, such as predicting future trends or making informed decisions based on data.

In summary, the slope of the line of best fit is a key indicator of the relationship between two variables, showing how one variable responds to changes in the other.