0.2,0.2,0.3,0.4,0.6,0.6,0.9,1.1,1.2,1.3,1.4
Mean:
To find the mean, add up all of the numbers and divide the sum by the number of numbers (which in this case is 11).
So the mean is roughly 0.745454545454545 which rounds to 0.75 (to the nearest hundredth)
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Median:
To find the median, count off about 5 spaces(it's close to half of the size of the list) until you hit the middle number. Now count count off about 5 spaces from the end of the list until you hit the middle.
0.2, 0.2, 0.3, 0.4, 0.6,
Median=0.6
So the median is 0.6
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Mode:
To find the mode, simply look for any repeating numbers. If there is one number that occurs more frequently than any other number, then that number is the mode.
In this case, the numbers 0.6 and 0.2 repeat themselves 2 times.
So the mode is 0.6 and 0.2
Note: you can have more than one mode
Range:
To find the range, subtract the smallest number (in this case 0.2) from the largest number (in this case 1.4) like this:
So the range is 1.2
Interquartile Range:
First, find the median for the first half of the data set (ignore the median):
Median of 0.2, 0.2, 0.3, 0.4, 0.6 is 0.3 (since this is the middle most value)
Now find the median of the last half of the data set (ignore the median):
Median of 0.9, 1.1, 1.2, 1.3, 1.4 is 1.2 (since this is the middle most value)
So the 1st and 3rd quartiles are 0.3 and 1.2 respectively.
This means that the interquartile range is
1.2 - 0.3 = 0.9
Note: the interquartile range is the difference between the 1st and 3rd quartiles
Note: I'm assuming that you want the sample standard deviation.
Standard Deviation:
Step 1) Find the mean:
So the mean is roughly 0.75 (rounded to the nearest hundredth).
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Step 2) Find the differences between the mean and every value:
Now subtract EVERY value from the mean (0.75) to get:
0.2-0.75 = -0.55, 0.2-0.75 = -0.55, 0.3-0.75 = -0.45, 0.4-0.75 = -0.35, 0.6-0.75 = -0.15, 0.6-0.75 = -0.15, 0.9-0.75 = 0.15, 1.1-0.75 = 0.35, 1.2-0.75 = 0.45, 1.3-0.75 = 0.55, 1.4-0.75 = 0.65,
So you should have a new column of values:
-0.55, -0.55, -0.45, -0.35, -0.15, -0.15, 0.15, 0.35, 0.45, 0.55, 0.65,
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Step 3) Squaring the differences:
Now square EVERY value in the last column to get:
(-0.55)^2 = 0.3, (-0.55)^2 = 0.3, (-0.45)^2 = 0.2, (-0.35)^2 = 0.12, (-0.15)^2 = 0.02, (-0.15)^2 = 0.02, (0.15)^2 = 0.02, (0.35)^2 = 0.12, (0.45)^2 = 0.2, (0.55)^2 = 0.3, (0.65)^2 = 0.42,
So you should have a new column of values:
0.3, 0.3, 0.2, 0.12, 0.02, 0.02, 0.02, 0.12, 0.2, 0.3, 0.42,
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Step 4) Adding up the squares:
Now add up the values of the last column:
0.3 + 0.3 + 0.2 + 0.12 + 0.02 + 0.02 + 0.02 + 0.12 + 0.2 + 0.3 + 0.42 = 2.02
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Step 5) Divide the sum by n-1:
Divide that sum by one less than the number of values in the list (in this case, it is n-1=11-1=10).
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Step 6) Take the square root of the quotient:
Take the square root of
Now round to the nearest hundredth to get
So the standard deviation is roughly
If you don't like that method, then here's another way:
First sort the numbers into ascending order (from least to greatest):
0.2,0.2,0.3,0.4,0.6,0.6,0.9,1.1,1.2,1.3,1.4
Standard Deviation:
So we can replace N with 11
Subtract
Replace
Expand the summation (replace each
Subtract the terms in the parenthesis
Square each term
Add up all of the terms
Multiply
Take the square root
Round to the nearest hundredth
So the standard deviation is roughly
25th and 75th percentiles:
The 25th and 75th percentiles are simply the first and third quartiles. We found these earlier to be 0.3 and 1.2.
So the 25th and 75th percentiles are 0.3 and 1.2