Q. Considering the mean, mode and skewness of data, the value of skewness will be negative if

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Q. In kurtosis, the beta is less than three and median is preferred as central tendency for

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Q. The first quartile of data set is 12, third quartile of data set is 18 and median is 9 then the absolute skewness of the same data set is

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Q. The 90th percentile is 60, 50th percentile is 30 and 10th percentile is 40 then the coefficient of skewness is

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Q. Considering the alpha and beta in moments, the measure of asymmetrical distribution is possible with

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Q. The distribution whose mode is not well defined and the classes of distribution are open ended uses the coefficient of skewness by

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Q. If the median is 18, coefficient of skewness is 6 and the mean is 30 then standard deviation of data is

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Q. If the first quartile and third quartile are as 20 and 18 respectively with the median of 12 then distribution is skewed to

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Q. Considering the moments in standard units, the third alpha with power 2 is equivalent to

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Q. The degree or extent to which the frequency of the observations in data set are concentrated in given frequency distribution is classified as

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Q. According to notations used by R.A. Fisher, the value of beta one with square root is equivalent to

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Q. The frequency distribution is considered as positively skewed if all the values of distribution moves to

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Q. The percentile and moment system are two groups of

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Q. In kurtosis, the frequency curve which is also classified as normal curve is called

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Q. As compared to measures of variation, the measure of skewness is used to understand concentration of

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Q. In moments, the alphas whose values depends on shape of frequency curve are

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Q. The coefficient of skewness by Karl Pearson, Professor Kelly and professor Bowley are considered as

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Q. If the values of skewness and arithmetic mean is given as 4 and 17 respectively then mode of the values is

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Q. If the median is 12, mean is 15 and the standard deviation of data is 3 then Karl Pearson's coefficient of skewness is

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Q. If all the values move towards one tail of a distribution then this scenario results in

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