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In: Statistics and Probability

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

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

Below is the scatterplot, regression line, and corresponding data for the height and weight of 11 randomly selected adults. You should notice something odd about the last entry.

         
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 the entire table.

Answer the following questions regarding the relationship.

(a) Using all 11 data pairs for height and weight, calculate the correlation coefficient. Round your answer to 3 decimal places.
r =

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

YesNo    


(c) Using only the first 10 data pairs for height and weight, calculate the correlation coefficient. Round your answer to 3 decimal places.
r =

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

YesNo    


(e) Which statement explains this situation?

The height for the last data pair must be an error.The erroneous value from the last data pair ruined a perfectly good correlation.    Despite the low correlation coefficient from part (a), there is probably a significant correlation between height and weight.All of these are valid statements.

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Expert Solution

Answer:

Using minitab>stat>basic stat>correlation

Correlation: height (x), weight (y)

Pearson correlation of height (x) and weight (y) = 0.237
P-Value = 0.483
a ) the correlation coefficient r = 0.237

b ) No , there is not a significant linear correlation between these 11 data pairs because p value is greater than 0.05

c ) using minitab>stat>basic stat>correlation

Correlation: height (x), weight (y)

Pearson correlation of height (x) and weight (y) = 0.924
P-Value = 0.000

the correlation coefficient r = 0.924

d ) Yes , there is a significant linear correlation between these 10 data pairs because p value is less than 0.05

e ) The erroneous value from the last data pair ruined a perfectly good correlation.  

[OR]

Solution:

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orchestra answered 1 year ago
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