Answer:
Here's what I get.
Step-by-step explanation:
1. Representation of data
I used Excel to create a scatterplot of the data, draw the line of best fit, and print the regression equation.
2. Line of best fit
(a) Variables
I chose arm span as the dependent variable (y-axis) and height as the independent variable (x-axis).
It seems to me that arm span depends on your height rather than the other way around.
(b) Regression equation
The calculation is easy but tedious, so I asked Excel to do it.
For the equation y = ax + b, the formulas are

This gave the regression equation:
y = 1.0595x - 4.1524
(c) Interpretation
The line shows how arm span depends on height.
The slope of the line says that arm span increases about 6 % faster than height.
The y-intercept is -4. If your height is zero, your arm length is -4 in (both are impossible).
(d) Residuals

The residuals appear to be evenly distributed above and below the predicted values.
A graph of all the residuals confirms this observation.
The equation usually predicts arm span to within 4 in.
(e) Predictions
(i) Height of person with 66 in arm span

(ii) Arm span of 74 in tall person

Answer:
144 is correct
Step-by-step explanation:
13 - 1 is 12
12 times 12 = 144
144 divided by 2 is 72
72 times 2 is 144
The reason for this has to do with finding the area of a square. When you are looking for the area of a square, you use the rectangle formula (since a square is also a rectangle).
The formula is Area = Length * Width
However, since in a square, the length and width are the same, both get changed to the word "Side". As a result, we get the following formula.
Area = Side * Side
This can be simplified to:
Area = Side^2.
Since each number to the second power also shows the area of a square with that given length sides, it can also be called "squared".