MCQ on Measure of Dispersion
What is Measure of Dispersion in Statistics?
Measures of dispersion in statistics refer to the numerical values that showcase the extent of variability or spread within a dataset. While measure of central tendency like mean, median, and mode provide a central value, measures of dispersion elucidate the degree to which data points deviate from this central value.
Why is Measure of Dispersion Important?
Measure of dispersion offer crucial insights into the variability of the data. They help in understanding how data points are spread out, providing a comprehensive view of the dataset’s characteristics. Understanding dispersion aids in making informed decisions, performing accurate analyses, and comprehending the reliability of statistical results.
Measure of dispersion offer crucial insights into the variability of data. They help in understanding how data points are spread out, providing a comprehensive view of the dataset’s characteristics. Understanding dispersion aids in making informed decisions, performing accurate analyses, and comprehending the reliability of statistical results.
What are Common Measure of Dispersion?
Range: This is the simplest measure and signifies the difference between the highest and lowest values in a dataset.
Variance: It measures how far each number in the dataset is from the mean, squared, and then averaged.
Standard Deviation: This quantifies the amount of variation or dispersion of a dataset. It’s the square root of the variance and provides a more interpretable measure than variance.
Interquartile Range (IQR): The IQR represents the range between the 25th and 75th percentiles of the dataset, providing a measure that is less influenced by outliers.
Coefficient of Variation: This dimensionless measure standardizes the variability, expressing the standard deviation as a percentage of the mean, allowing comparison of variability between different datasets.
