Height vs Weight - Erroneous Data: You willneed to use software to answer these questions.

Below is the scatterplot, regression line, and corresponding datafor the height and weight of 11 randomly selected adults. Youshould notice something odd about the last entry.

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index height (x) weight (y) inches pounds 1 60 120 2 72 200 3 65 130 4 71 205 5 69 180 6 68 180 7 69 193 8 69 195 9 63 115 10 62 140 11 5.5 160

You should be able copy and paste the data by highlighting theentire table.

Answer the following questions regarding the relationship.

(a) Using all 11 data pairs for height and weight, calculate thecorrelation coefficient. Round your answer to 3 decimalplaces.
r =

(b) Is there a significant linear correlation between these 11 datapairs?

YesNo    

(c) Using only the first 10 data pairs for height and weight,calculate the correlation coefficient. Round your answer to3 decimal places.
r =

(d) Is there a significant linear correlation between these 10 datapairs?

YesNo    

(e) Which statement explains this situation?

The height for the last data pair must be an error.The erroneousvalue from the last data pair ruined a perfectly goodcorrelation.    Despite the low correlationcoefficient from part (a), there is probably a significantcorrelation between height and weight.All of these are validstatements.

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