Finding the Median for Bidding Values of Each Team

Median is the middle value in a set of data. It is used to measure central tendency and is a better measure of central tendency than mean when the data has extreme values or outliers. In this question, we are asked to find the median for the bidding values of each team. Let's see how we can do that.

Understanding the Data Set

Before finding the median, we need to understand the data set. We are given the bidding values of each team. We need to find the median of these values. Let's assume that the bidding values for each team are as follows:

Chennai Super Kings: 10, 20, 30, 40, 50
Mumbai Indians: 15, 25, 35, 45, 55
Royal Challengers Bangalore: 5, 15, 25, 35, 45

Finding the Median

To find the median, we need to arrange the data set in ascending or descending order. Once the data set is arranged, we can easily find the middle value. If there are even number of values, we take the average of the two middle values. Let's find the median for each team.

Median for Chennai Super Kings

The bidding values for Chennai Super Kings are:
10, 20, 30, 40, 50

Arranging these values in ascending order:
10, 20, 30, 40, 50

The middle value is 30. Therefore, the median for Chennai Super Kings is 30.

Median for Mumbai Indians

The bidding values for Mumbai Indians are:
15, 25, 35, 45, 55

Arranging these values in ascending order:
15, 25, 35, 45, 55

The middle value is 35. Therefore, the median for Mumbai Indians is 35.

Median for Royal Challengers Bangalore

The bidding values for Royal Challengers Bangalore are:
5, 15, 25, 35, 45

Arranging these values in ascending order:
5, 15, 25, 35, 45

The middle value is 25. Therefore, the median for Royal Challengers Bangalore is 25.

Conclusion

In conclusion, we have found the median for the bidding values of each team. The median for Chennai Super Kings is 30, for Mumbai Indians is 35, and for Royal Challengers Bangalore is 25. Median is a better measure of central tendency than mean when the data has extreme values or outliers. It gives us a better idea of the central value of the data set.