![]() ![]() So, to find out the IQR first we have to sort the data on ascending order as the data is already sorted so we don’t need to sort it. #Measure of dispersio how to#Let’s understand how to find the interquartile range: To calculate IQR first we have to sort the data in ascending order. IQR is also very helpful to determine the outlier in the datasets. As the IQR goes up the data points are more spread out and if the IQR is small they assumed to be data is spread around the mean. It means IQR measure the spread of the middle 50% of the dataset. It measures the difference between the third quartile and the first quartile of the data. #Measure of dispersio series#The interquartile range is a measure of dispersion, as it also measures the variability of the data, IQR indicates how the data in a series is dispersed from the mean. This temperature is an important measure when the temperature was one of the deciding factors for the open-air events. HourĪs the table shows the temperature which is measured every three hours, the green highlighted row shows the minimum value for the temperature was 6 ⁰C at 3.00 hours and the red highlighted row shows the maximum value for the temperature was 27 ⁰C at 15.00 hours. ![]() Let’s understand with an example of weather report, the temperature is measured every three hours during a given day. ![]() The range is helpful when you want to focus on extreme values in the dataset. It offers a crude insight into the spread of the data, but very susceptible to outliers. The simplest measure of dispersion is Range it is the difference between the highest value and lowest value in the dataset. In this blog we will discuss about four commonly used measures of dispersion. Measures Of dispersion is also known as “Measures of Variability” because it indicates the variability of the data that how much we still do not know about the data. Measures of dispersion indicate how the data is spread or scattered from the measures of central tendency. One essential measure is how the data is scattered or dispersed. To understand the data well, only studying measures of central tendency is not enough. ![]()
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