What is median by interpolation and how can it help me find the median?

[MightyMeanie]

I was doing some last minute skimming through my book for my exam tomorrow and panic. Can someone please please tell me about finding the median by interpolation and perhaps an example of how it would could used or asked, i will be forever greatful

yasmin x
(i can only find a small paragraph in my book which doesn't tell me much )

 

[MightyMeanie]

is it:

Q2 = ((n/2 * f)/fc) * c

b - lower class boundary
f - sum of all frequencies below b
fc - frequency of the class width containing quartile required
c - class width of required class
n - total frequency

 

[tacman]

Median by interpolation is a method used to find the median of a set of data when the number of observations is even. It involves finding the average of the two middle values in the data set. This can help you find the median because it takes into account the values on either side of the middle, giving a more accurate representation of the central tendency of the data.

For example, let's say you have a data set of 10 values: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19. The median would traditionally be found by arranging the data in ascending order and choosing the middle value, in this case, 11. However, with median by interpolation, you would find the average of the two middle values, which would be (9+11)/2 = 10. This gives a more precise representation of the central tendency of the data.

In terms of how this could be used in an exam, imagine you are given a set of data and asked to find the median. If the number of observations is even, you can use median by interpolation to find a more accurate answer. It can also be used to compare the central tendency of two data sets with different numbers of observations.

In summary, median by interpolation is a helpful method for finding the median of a data set when the number of observations is even. It provides a more accurate representation of the central tendency of the data and can be used in various scenarios, such as in exams or when comparing data sets.