Billy left home at 9 am and rode his bike to the park at an average speed of 10 miles per hour. He arrived at the park at 9:30 a
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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
