Measures of dispersion Flashcards

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1
Q

What are measures of dispersion?

A

A set of data can be described in terms of how dispersed or spread out the data ites are

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2
Q

What is the range?

A

The range is the arithmetic distance between the top and bottom values in a set of data
Once you have difference you have to add 1

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3
Q

Why do we add an additional 1 to the range?

A

because the bottom number of 3 could represent a value as low as 2.5 and top number 15 could be as high as 15.5

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4
Q

Calculate the range of this- 3, 5, 8, 8, 9, 10, 12, 12, 13, 15?

A

range 13

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5
Q

Strengths of the range?

A

identify the spread out of data is accounts for end values of data set

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6
Q

Limitations of the range?

A

-If affected by extreme values as values are at highest and lowest end of set are used
-Meaning it may not actually reflect data
-Fails to take into account distribution of numbers meaning it doesn’t indicate weather most numbers are closely grouped or evanly groupes

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7
Q

What is standard deviation?

A

measures if the spread of the set of numbers, in effect the average distance of each number from the mean

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8
Q

What is step 1 of standard deviation?

A

Calculate the mean

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9
Q

What is step 2 of standard deviation?

A

Subtracting mean for each raw score

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10
Q

What is step 3 of standard deviation?

A

Square the numbers from step 2

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11
Q

What is step 4 of standard deviation?

A

Add number from step 3 together

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12
Q

What is step 5 of standard deviation?

A

Divide step 4 by amount of raw score

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13
Q

What is step 6 of standard deviation?

A

square root step 5 to get the answer

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14
Q

Strengths of standard deviation?

A

-Takes precise measures of dispersion allowing all values are taken into account mean we know weather values are closer group around means or spread out
-May hide characteristics of data, like extreme values as it indicates how far each value is rather then looking at values at end of data set

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15
Q

Limitations of standard deviation?

A

-may hide characteristics of data like extreme values as it indicates how far each value is rather then looking at values at each end of data set

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