In statistics, we interpret the slope of the regression line as the estimated change in y per unit increase in x. In our temperature example, the units are degrees Fahrenheit, so we interpret our value $b_1=1.4$ as follows:

“For each increase of 1°F in low temperature, the estimated high temperature increases by 1.4°F.”

The $y$ intercept is interpreted as the estimated value of y when x equals zero. Here, we interpret our value $b_0=8$ as follows:

“When the low temperature is 0°F, the estimated high temperature is 8°F.”