The distance (in kilometers) and price (in dollars) for one-way airline tickets from San Francisco to several cities are shown in the table. $$\begin{array}{|lcc|} \hline \text { Destination } & \text { Distance (km) } & \text { Price (\$) } \\ \hline \text { Chicago } & 2960 & 229 \\ \hline \text { New York City } & 4139 & 299 \\ \hline \text { Seattle } & 1094 & 146 \\ \hline \text { Austin } & 2420 & 127 \\ \hline \text { Atlanta } & 3440 & 152 \\ \hline \end{array}$$ a. Find the correlation coefficient for this data using a computer or statistical calculator. Use distance as the $x$ -variable and price as the $y$ -variable. b. Recalculate the correlation coefficient for this data using price as the $x$ -variable and distance as the $y$ -variable. What effect does this have on the correlation coefficient? c. Suppose a $$\$ 50$$ security fee was added to the price of each ticket. What effect would this have on the correlation coefficient? d. Suppose the airline held an incredible sale, where travelers got a round-trip ticket for the price of a one-way ticket. This means that the distances would be doubled while the ticket price remained the same. What effect would this have on the correlation coefficient?
Added by Lisa C.
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Given data: - Distance (x): 2960, 4139, 1094, 2420, 3440 - Price (y): 229, 299, 146, 127, 152 Using a calculator, we find the correlation coefficient to be approximately 0.652. ** Show more…
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