As yet another earthquake rattled her china cabinet, the data scientist decided to test whether hydraulic fracturing (where water is injected into the Earth)truly was predictive of the number of earthquakes in the region. What is the slope of the regression equation based on the data? $$\begin{array} { | c | c | }
\hline \text { Injections } & \text { Earthquakes } \
\hline 137 & 682 \
\hline 331 & 833 \
\hline 360 & 905 \
\hline 442 & 1008 \
\hline 478 & 1482 \
\hline 529 & 1742 \
\hline
\end{array}$$
A)151.24
B)2.52
C)0.85
D)0.72

Question

As yet another earthquake rattled her china cabinet, the data scientist decided to test whether hydraulic fracturing (where water is injected into the Earth)truly was predictive of the number of earthquakes in the region. What is the slope of the regression equation based on the data? $$\begin{array} { | c | c | } 
\hline \text { Injections } & \text { Earthquakes } \
\hline 137 & 682 \
\hline 331 & 833 \
\hline 360 & 905 \
\hline 442 & 1008 \
\hline 478 & 1482 \
\hline 529 & 1742 \
\hline
\end{array}$$
A)151.24
B)2.52
C)0.85
D)0.72

Quizplus · Accepted Answer

The slope ($m$) of the regression line can be calculated using the formula: $$m = \frac{n(\sum xy) - (\sum x)(\sum y)}{n(\sum x^2) - (\sum x)^2}$$ where $n$ is the number of observations, $x$ represents the injections, and $y$ represents the earthquakes. Plugging the given data into this formula gives a slope of approximately 2.52.

As Yet Another Earthquake Rattled Her China Cabinet, the Data