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2.5.6 Pearson's product-moment correlation coefficient

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Question 48

An aerospace engineer is testing the performance of a high-altitude surveillance drone. She records the maximum ascent speed, vvv m/s, of the drone while carrying various payload weights, www kg. Data from 6 test flights are recorded in the table below:

w (kg)13581216v (m/s)20181613117\begin{array}{|c|c|c|c|c|c|c|} \hline w \text{ (kg)} & 1 & 3 & 5 & 8 & 12 & 16 \\ \hline v \text{ (m/s)} & 20 & 18 & 16 & 13 & 11 & 7 \\ \hline \end{array}w (kg)v (m/s)​120​318​516​813​1211​167​​

[You may use: ∑\sum∑ w = 45, ∑\sum∑ v = 85, ∑\sum∑ w^2 = 499  and \text{ and } and  ∑\sum∑ wv = 502]

a.

Explain why a linear regression model might be suitable for this data.

[1]
b.

Calculate the value of SwvS_{wv}Swv​ and the value of SwwS_{ww}Sww​.

[2]
c.

Find the equation of the regression line of vvv on www, giving your answer in the form v=a+bwv = a + bwv=a+bw. Give your values to 3 significant figures.

[3]
d.

A specific sensor suite requires a payload of 10 kg. Estimate the maximum ascent speed for the drone when equipped with this suite.

[1]

2.5.6 Pearson's product-moment correlation coefficient Questions

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