McGraw Hill Glencoe Algebra 2, 2012
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McGraw Hill Glencoe Algebra 2, 2012 View details
9. Measures of Center, Spread, and Position
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Exercise 11 Page P28

Practice makes perfect
a Identifying an outlier involves several steps. We need to find the lower and upper quartiles and look for values that are beyond these by more than 1.5 times the interquartile range. We can use a calculator to find the one-variable statistics for the data set.

To start, press the STATbutton. Choose EDIT to enter the data and once it is done, press the STAT button again and choose CALC to find the one-variable statistics.

The results are shown on two screens.

To find the lower and upper quartiles, you need to scroll down to the second screen. Lower Quartile: Q_1= 16.1 Upper Quartile: Q_3= 16.6 The interquartile range is the difference of the upper and lower quartiles. Interquartile Range: IQR= 16.6- 16.1= 0.5 The next step is to find the values beyond which any outliers would lie. To get these values we subtract 1.5 times the interquartile range from the lower quartile and add the interquartile range to the upper quartile. Q_1-1.5* IQR= 16.1-1.5( 0.5)=15.35 Q_3+1.5* IQR= 16.6+1.5( 0.5)=17.35 An outlier is a value either less than 15.35 or greater than 17.35. 16.7,16.8,15.9,16.1,16.5,16.6,16.5,15.9,16.7,16.5 16.6,14.9,16.5,16.1,15.8,16.7,16.2,16.5,16.4,16.6 There is one such value, 14.9. It is the only outlier.

b If the outlier, 14.9, is replaced by the new value, 17.35, we get the following data set.

16.7,16.8,15.9,16.1,16.5,16.6,16.5,15.9,16.7,16.5 16.6,17.35,16.5,16.1,15.8,16.7,16.2,16.5,16.4,16.6To check whether the new value is an oulier, we need to repeat the process of part A with the new value instead. It is not enough to just use the bounds from part A, since replacing a value changes the statistics. Lower Quartile: Q_1= 16.15 Upper Quartile: Q_3= 16.65 Interquartile Range: IQR= 16.65- 16.15= 0.5 Using these values, we get the bound for outliers. Q_1-1.5* IQR= 16.15-1.5( 0.5)=15.4 Q_3+1.5* IQR= 16.65+1.5( 0.5)=17.4 Since 15.4<17.35<17.4, this added data is not an outlier.

c Let's think about what could happen to create an extremely low or high value hen recording the weight of the box of cereals.
  • The machine could have filled the box not according to the expected standards.
  • We could have measured the box incorrectly.
  • The measurement could be correct, we just recorded the value incorrectly.