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Bowley’s Coefficient of Skewness, Business Mathematics & Statistics Video Lecture | SSC CGL Tier 2 - Study Material, Online Tests, Previous Year

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FAQs on Bowley’s Coefficient of Skewness, Business Mathematics & Statistics Video Lecture - SSC CGL Tier 2 - Study Material, Online Tests, Previous Year

1. What is Bowley's coefficient of skewness?
Ans. Bowley's coefficient of skewness is a measure of the asymmetry or lack of symmetry in a dataset. It is calculated by dividing the difference between the median and the mean by the semi-interquartile range. A positive coefficient indicates a right-skewed distribution, while a negative coefficient indicates a left-skewed distribution.
2. How is Bowley's coefficient of skewness calculated?
Ans. To calculate Bowley's coefficient of skewness, follow these steps: 1. Find the median of the dataset. 2. Calculate the lower quartile (Q1) and the upper quartile (Q3). 3. Find the semi-interquartile range (SIQR) by subtracting Q1 from Q3 and dividing by 2. 4. Calculate the difference between the median and the mean. 5. Divide the difference by the semi-interquartile range to obtain Bowley's coefficient of skewness.
3. What does a positive Bowley's coefficient of skewness indicate?
Ans. A positive Bowley's coefficient of skewness indicates a right-skewed distribution. In this type of distribution, the tail on the right side of the dataset is longer or more spread out than the tail on the left side. The mean is typically larger than the median in a right-skewed distribution.
4. What does a negative Bowley's coefficient of skewness indicate?
Ans. A negative Bowley's coefficient of skewness indicates a left-skewed distribution. In this type of distribution, the tail on the left side of the dataset is longer or more spread out than the tail on the right side. The mean is typically smaller than the median in a left-skewed distribution.
5. How can Bowley's coefficient of skewness be used in business and statistics?
Ans. Bowley's coefficient of skewness is a useful tool in business and statistics to analyze the shape and symmetry of datasets. It helps in understanding the distribution of data and identifying any potential outliers or unusual patterns. This information can be valuable in making informed decisions, such as determining the appropriate pricing strategy for a product or assessing the risk associated with a financial investment.
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