Changes in data can really affect the slope of a linear regression line. We call the slope $b_1$ in the equation $y = b_0 + b_1x$.

Let’s break it down:

1. Outliers: Sometimes, data can have extreme values that stand out. If these outliers are high on both axes, they can make the slope steeper. But if they are low, they can make the slope less steep.

2. Variation of Data: If the $x$ values have a lot of differences, this can lead to a steeper slope. A steeper slope means a stronger relationship between the $x$ and $y$ values, which we show with the correlation coefficient ($r$).

3. Sample Size: When we have more data points, it helps make the slope more stable. More data points can reduce the amount we might see changes in the slope and make our findings more dependable.