Mean Median Mode

Mean 

Might be we heard about “average” in our day to day life, In statistical world average is Mean, where you add up all the numbers and then divide by the count of numbers/data points.

Median 

is the “middle” value in the list of numbers. To find the median, your numbers/data points have to be listed in numerical order from smallest to largest or vise versa,

 Mode 

is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list.

Mode can be used in Categorical variables where Mean and Median will be used only for numerical values

Example:

 Let’s take the below dataset of ages for students..

Ages = (10,11,12,14,11,16,18,13,15,90)

Now using this dataset let’s calculate Mean, Median, Mode.

Mean:

        1)Add all values in dataset = 10+11+12+14+11+16+18+13+15+90 = 210

        2)Now divide by the number of dataset = 210/10= 21

So the mean of the given dataset is 21

Median:

1. Order your cases from smallest to largest  (10,11,11,12,13,14,15,16,18,90)
2. Find the middle Value

• If you have odd number of cases then finding middle value is easy. Let’s think you have 5 cases. So, after ordering always 3rd position is the middle value.
• If you have even number no cases (let’s think 6 cases). In this case there is no single middle value. Then how do we calculate median? Well, we just take the average of the two middle values.

Mode:

Finding the mode is easy. Basically, it is the value that occurs most frequently.  In other words, mode is the most common outcome. Mode is the name of the category that occurs more often.

There is a chance of having more than one mode in your datasets.

In our case 11 is the frequent occurring data point. .

Very Important points to remember on 3M(Mean,Median,Mode)

1.  In our above age dataset 90 is the maximum age and the other ages are less than 20, So here age 90 is called Outlier(We will discuss more on outliers in this blog)
2.  Outliers are very serious problems in statistics world, We should be very careful in dealing with Outliers.
3. It can mislead the final outputs
4. If we have outliers in our dataset then go for Median as median will never affect of outliers as we are taking middle value.