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Questions and Answers
What is the coefficient of variation (CV) used for?
What is the coefficient of variation (CV) used for?
- To determine the presence of outliers
- To describe the distribution of the data set
- To measure relative variation (correct)
- To measure absolute variation
What does kurtosis measure in a distribution?
What does kurtosis measure in a distribution?
- Central tendency of the distribution
- Tailedness of the distribution (correct)
- Dispersion of the distribution
- Skewness of the distribution
When does a coefficient of variation (CV) greater than 1 indicate?
When does a coefficient of variation (CV) greater than 1 indicate?
- Low dispersion in the data set
- Symmetric distribution
- Presence of high erratic values (outliers) (correct)
- Normal distribution of data set
What does a meso-kurtic distribution represent?
What does a meso-kurtic distribution represent?
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How are skewness values interpreted?
How are skewness values interpreted?
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Study Notes
Measures of Dispersion and Distribution Shape
- The coefficient of variation (CV) is a statistical measure used to assess the relative variability of a dataset in relation to its mean value.
- CV is useful for comparing the variability of different datasets with different units or scales.
Kurtosis
- Kurtosis measures the tailedness or peakedness of a distribution, with high kurtosis indicating a more peaked distribution and low kurtosis indicating a flatter distribution.
Coefficient of Variation (CV)
- A CV greater than 1 indicates that the standard deviation is greater than the mean, which can occur in cases where the data contains extreme outliers or a skewed distribution.
Distribution Shapes
- A meso-kurtic distribution represents a normal or bell-shaped curve, which is neither too peaked nor too flat.
Skewness
- Skewness values can be interpreted as follows:
- Positive skewness indicates a distribution with a long tail to the right.
- Negative skewness indicates a distribution with a long tail to the left.
- A skewness value close to zero indicates a relatively symmetrical distribution.
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