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| 2.3 Measures of the Location of the Data |
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| The common measures of location are quartiles and percentiles. |
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| Quartiles are special percentiles. |
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| The first quartile, Q1, is the same as the 25th percentile, and the third quartile, Q3, is the same as the 75th percentile. |
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| The median, M, is called both the second quartile and the 50th percentile. |
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| To calculate quartiles and percentiles, you must order the data from smallest to largest. |
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| Quartiles divide ordered data into quarters. |
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| Percentiles divide ordered data into hundredths. |
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| Recall that a percent means one-hundredth. |
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| So, percentiles mean the data is divided into 100 sections. |
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| To score in the 90th percentile of an exam does not mean, necessarily, that you received 90 percent on a test. |
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| It means that 90 percent of test scores are the same as or less than your score and that 10 percent of the test scores are the same as or greater than your test score. |
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| Percentiles are useful for comparing values. |
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| For this reason, universities and colleges use percentiles extensively. |
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| One instance in which colleges and universities use percentiles is when SAT results are used to determine a minimum testing score that will be used as an acceptance factor. |
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| For example, suppose Duke accepts SAT scores at or above the 75th percentile. |
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| That translates into a score of at least 1220. |
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| Percentiles are mostly used with very large populations. |
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| Therefore, if you were to say that 90 percent of the test scores are less, and not the same or less, than your score, it would be acceptable because removing one particular data value is not significant. |
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| The median is a number that measures the center of the data. |
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| You can think of the median as the middle value, but it does not actually have to be one of the observed values. |
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| It is a number that separates ordered data into halves. |
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| Half the values are the same number or smaller than the median, and half the values are the same number or larger. |
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| For example, consider the following data: |
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| 1, 11.5, 6, 7.2, 4, 8, 9, 10, 6.8, 8.3, 2, 2, 10, 1 |
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| Ordered from smallest to largest: |
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| 1, 1, 2, 2, 4, 6, 6.8, 7.2, 8, 8.3, 9, 10, 10, 11.5 |
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| When a data set has an even number of data values, the median is equal to the average of the two middle values when the data are arranged in ascending order (least to greatest). |
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| When a data set has an odd number of data values, the median is equal to the middle value when the data are arranged in ascending order. |
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| Since there are 14 observations (an even number of data values), the median is between the seventh value, 6.8, and the eighth value, 7.2. |
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| To find the median, add the two values together and divide by two. |
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| (6.8+7.2)/2=7 |
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| The median is seven. |
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| Half of the values are smaller than seven and half of the values are larger than seven. |
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| Quartiles are numbers that separate the data into quarters. |
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| Quartiles may or may not be part of the data. |
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| To find the quartiles, first find the median, or second, quartile. |
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| The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. |
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| To get the idea, consider the same data set: |
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| 1, 1, 2, 2, 4, 6, 6.8, 7.2, 8, 8.3, 9, 10, 10, 11.5 |
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| The data set has an even number of values (14 data values), so the median will be the average of the two middle values (the average of 6.8 and 7.2), which is calculated as (6.8+7.2)/2 and equals 7. |
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| So, the median, or second quartile (Q2), is 7. |
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| The first quartile is the median of the lower half of the data, so if we divide the data into seven values in the lower half and seven values in the upper half, we can see that we have an odd number of values in the lower half. |
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| Thus, the median of the lower half, or the first quartile (Q1) will be the middle value, or 2. |
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| Using the same procedure, we can see that the median of the upper half, or the third quartile (Q3) will be the middle value of the upper half, or 9. |
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| The quartiles are illustrated below: |
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| Figure 2.11 |
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| The interquartile range is a number that indicates the spread of the middle half, or the middle 50 percent of the data. |
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| It is the difference between the third quartile (Q3) and the first quartile (Q1) |
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| IQR = Q3 – Q1. |
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| The IQR for this data set is calculated as 9 minus 2, or 7. |
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| The IQR can help to determine potential outliers. |
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| A value is suspected to be a potential outlier if it is less than 1.5 × IQR below the first quartile or more than 1.5 × IQR above the third quartile. |
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| Potential outliers always require further investigation. |
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| NOTE |
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| A potential outlier is a data point that is significantly different from the other data points. |
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| These special data points may be errors or some kind of abnormality, or they may be a key to understanding the data. |
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| Example 2.15 |
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| For the following 13 real estate prices, calculate the IQR and determine if any prices are potential outliers. |
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| Prices are in dollars. |
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| 389,950; 230,500; 158,000; 479,000; 639,000; 114,950; 5,500,000; 387,000; 659,000; 529,000; 575,000; 488,800; 1,095,000 |
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| Try It 2.15 |
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| For the 11 salaries, calculate the IQR and determine if any salaries are outliers. |
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| The following salaries are in dollars. |
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| $33,000; $64,500; $28,000; $54,000; $72,000; $68,500; $69,000; $42,000; $54,000; $120,000; $40,500 |
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| In the example above, you just saw the calculation of the median, first quartile, and third quartile. |
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| These three values are part of the five number summary. |
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| The other two values are the minimum value (or min) and the maximum value (or max). |
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| The five number summary is used to create a box plot. |