Answer:
Graph A = 20miles/gallon
Graph B = 20
Step-by-step explanation:
For graph A, to find the change you will need to choose two points from the graph (the easier the better)
So for example, I've chosen 40miles-2gallons (2, 40) and point 0 (0, 0)
Now you will need to remember the slope formula which is:

so your point (x1, y1) will be (0, 0)
and point (x2, y2) will be (2, 40)
so 
which is 
and so you get that the slope will be 20 miles/gallon
For graph B, what they want you to see is that change of y/change of x means the same as change of miles/change of gallons since x is the gallons and y is the miles.
Answer:
Neither
Step-by-step explanation:
If the sequence was geometric, each term would be multiplied by the same multiplier to get to the next one. We can check if the multiplier is the same by taking a term and dividing it by the term before it. For example,
-4/3.5=-1.14285714
-7.5/-4=1.87500
The multiplier between the terms aren't the same so it's not geometric.
For arithmetic, the distances between each term would be the same, and we can take the same idea from the geometric sequence, but use subtraction instead of division
-4-3,5=-7.5
-7.5-(-4)=-3.5
Again, the distances aren't the same, so it's not arithmetic.
Step-by-step explanation:
Regression analysis is used to infer about the relationship between two or more variables.
The line of best fit is a straight line representing the regression equation on a scatter plot. The may pass through either some point or all points or none of the points.
<u>Method 1:</u>
Using regression analysis the line of best fit is: 
Here <em>α </em>= intercept, <em>β</em> = slope and <em>e</em> = error.
The formula to compute the intercept is:

Here<em> </em>
and
are mean of the <em>y</em> and <em>x</em> values respectively.

The formula to compute the slope is:

And the formula to compute the error is:

<u>Method 2:</u>
The regression line can be determined using the descriptive statistics mean, standard deviation and correlation.
The equation of the line of best fit is:

Here <em>r</em> = correlation coefficient = 
and
are standard deviation of <em>x</em> and <em>y</em> respectively.
