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Explanation:
Multiply each visit count with their corresponding frequency.
Examples:
- 0*12 = 0 for the first row.
- 1*366 = 366 for the second row
- 2*53 = 106 for the third row
and so on...
I recommend making a third column like this
That way you can keep track of all the results in an organized way.
Then add everything in the third column
0+366+106+156+620+1215 = 2463
Divide this sum over the total frequency (12+366+53+52+155+243 = 881) and we'll get the mean
2463/881 = 2.7956867
Rounding to one decimal place gets us to 2.8 as the final answer.
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The much longer way to do this is to imagine 12 copies of "0", 366 copies of "1", 53 copies of "2", and so on. We'll have an extremely large data set of 881 items inside it. As you can see, this second method is definitely not recommended to actually carry out. Rather it's helpful to have this as a thought experiment to see why we revert to multiplication instead.
Eg: Imagine adding 155 copies of "4". A shortcut is to simply say 4*155 = 620
