![]() Q 3 = As there are 5 values in the upper half, the Q 3 will be 77 as it is a middle value of the upper half. As we know, 10 is an even number so the median is mean of 70 and 72 ![]() There are a total of 10 values in the above data. ![]() Q 1 = As there are 5 values in the lower half, so the Q 1 will be 64 as it is a middle value of the lower half Inter-Quartile Example on the Basis of the Above Definition Let's calculate the interquartile range of the below data So, there are a total of 3 quartiles The first quartile also known as the lower quartile is represented by Q 1, the second quartile is represented by Q 2, and the last third quartile also known as the upper quartile is represented by Q 3. As we know quartiles are the divided values that divide the complete series into four equal parts. The interquartile definition states that the interquartile range is the difference between the third and the first quartiles. Interquartile range Definition and Example And all the points that fall beyond the outer fences are known as extreme outliers. Q 3 is the middle value in the second half.Īny data point that is going to fall between the inner and outer fences is said to be the mid-outliners. Q 1 here is the middle value in the first half If we talk about fences then there are four relevant fences. From the data or on a box plot a fence is used to identify and categorize the type of outliers. The interquartile range is often used to measure or find the outliers in the data. The interquartile range is equal to quartile 3 minus quartile 1. Q 3 - It is the middle value in the second half of the rank-ordered data set. Q 1 - It is the middle value in the first half of the rank-order data And they are represented by Q 1, Q 2, and Q 3. The values that split each part are known as the first, second, and third quartile. Quartile divides the range of data into four equal parts. The interquartile range is a measure of variability based on splitting data into quartiles. The interquartile range can be used to denote or indicate the variability of the set provided to you. The interquartile range is used to explain the difference between the upper and lower quartiles in the set of data. Instead of data, there are two quartiles, namely the upper quartile and the lower quartile. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.The interquartile range is actually the measure of static depression and the spread of the data. NoteTo get a clear idea of your data’s variability, the range is best used in combination with other measures of variability like interquartile range and standard deviation. It can’t tell you about the shape of the frequency distribution of values on its own. Although we have a large range, most values are actually clustered around a clear middle.īecause only two numbers are used, the range is easily influenced by outliers. In the example above, the range indicates much more variability in the data than there actually is. With an outlier, our range is now 42 years. Using the same calculation, we get a very different result this time: Range example with an outlierOne value in your data set is replaced with an outlier. One extreme value in the data will give you a completely different range. When paired with measures of central tendency, the range can tell you about the span of the distribution.īut the range can be misleading when you have outliers in your data set. The range generally gives you a good indicator of variability when you have a distribution without extreme values. Then subtract the lowest from the highest value. Participantįirst, order the values from low to high to identify the lowest value ( L) and the highest value ( H). Range exampleYour data set is the ages of 8 participants. This process is the same regardless of whether your values are positive or negative, or whole numbers or fractions.
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