Answer:
the answer is d
Step-by-step explanation:
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:
153 coins will be there.
Step-by-step explanation:
The number of coin in the bottom row / or last row = 17
The number of coins in the second last row = 16
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The number of coins in the second row = 2
The number of coins in the first row = 1
So the total number of coins = (Number of coin in the first row) + (Number of coins in the second row) + (Number of coins in the third row) + ....... + (Number of coins in the last row / seventeenth row)
Total number of coins = 1+2+3+....+16+17
Total number of coins = 
(NOTE: Sum of first n natural number =
)
B is the valid use of the associative property of addition