Lesson 5 Measures of variability Flashcards

1
Q
  • Are measures of the average distance of each observation from the center of the distribution.
  • They measure the homogeneity or heterogeneity of a particular group
A

Measures of variability or dispersion

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2
Q

A small measure of variability would indicate that the data are:

A
  1. clustered closely around the mean
  2. more homogeneous
  3. less variable
  4. more consistent
  5. more uniformly distributed.
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3
Q

Measures of Variability or Dispersion

A

Range (R)
Mean Absolute Deviation (MAD)
Variance
Standard Deviation
Coefficient of Variation (cv)

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4
Q

This is the simplest but the most unreliable measure of variability since it uses only two values in the distribution

A

Range (R)

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5
Q

Disadvantages of Range

A
  1. For a very large sample, it is an unstable descriptive measure of dispersion.
  2. Since only two values are used in the computation, the range is an unreliable measure of dispersion.
  3. The range of two sets of data composed of different numbers of samples are not directly comparable
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6
Q

is the simplest measure of variability that takes into account all data in the distribution

A

Mean Absolute Deviation (MAD)

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7
Q

Disadvantages of MAD

A
  1. Since the mean is used in its computations, then it is also greatly affected by extreme values
  2. Its use in further statistical computation is very limited
  3. It is not amenable to algebraic manipulation because of the use of absolute values.
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8
Q

are both reliable measures of variability or spread of the distribution

However, we cannot use them in comparing two sets of data of different units

A

SD and Variance

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9
Q

is the ratio of the standard deviation to the mean.

It is used to compare the variability of two or more sets of data even when they are expressed in different units of measurement.

A

Coefficient of variation (cv)

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10
Q

is the square root of the average deviation from the mean, or simply the square root of the variance.

A

Standard Deviation

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11
Q

is the average of the squares of the differences between the individual (observed) and the expected value

A

Variance

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12
Q

is a distribution with a bell-shaped appearance.

A

Normal distribution

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13
Q

refers to the degree of symmetry or asymmetry of a distribution

A

Skewness

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14
Q

If a distribution is skewed to the left

A

mean is less than its median
(mean < median)
negatively skewed

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15
Q

If a distribution is skewed to the right

A

mean is greater than its median
(mean > median)
positively skewed

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16
Q

The extent of skewness can be obtained by getting the

A

coefficient of skewness

17
Q

SK = 0

A

distribution is NORMAL

18
Q

SK < 0

A

distribution is SKEWED to the LEFT

19
Q

SK > 0

A

distribution is SKEWED to the RIGHT

20
Q

refers to the peakedness or flatness of a distribution

A

Kurtosis

21
Q

Kurtosis

is a normal distribution

A

Mesokurtic

22
Q

Kurtosis

is more peaked than the normal distribution.

A

Leptokurtic

23
Q

Kurtosis

is flatter than the normal distribution.

A

Platykurtic

24
Q

Ku > 3

A

Leptokurtic

25
Q

Ku < 3

A

Platykurtic

26
Q

Ku = 3

A

Mesokurtic