CA Foundation Exam > CA Foundation Videos > Crash Course for CA Foundation > Measures of Central Tendency and Dispersion - 1
Measures of Central Tendency and Dispersion - 1 Video Lecture | Crash Course for CA Foundation
FAQs on Measures of Central Tendency and Dispersion - 1 Video Lecture - Crash Course for CA Foundation
| 1. What are the main measures of central tendency? |  |
Ans. The main measures of central tendency are the mean, median, and mode. The mean is the average of a set of numbers, calculated by adding all values and dividing by the number of values. The median is the middle value when the numbers are arranged in order. The mode is the value that appears most frequently in the data set.
| 2. How do you calculate the mean, median, and mode? | |
Ans. To calculate the mean, sum all the values and divide by the total number of values. For the median, arrange the numbers in ascending order; if there's an odd number of values, the median is the middle one, and if even, it's the average of the two middle values. The mode is found by identifying the number that occurs most often in the data set.
| 3. What is the importance of measures of dispersion? | |
Ans. Measures of dispersion, such as range, variance, and standard deviation, are important because they provide insight into the variability or spread of data. They help to understand how much the data points differ from the central tendency, indicating the consistency or variability in the dataset.
| 4. What is the difference between range and standard deviation? | |
Ans. The range is the difference between the highest and lowest values in a data set, giving a simple measure of spread. Standard deviation, on the other hand, measures how much individual data points deviate from the mean, providing a more comprehensive understanding of data variability.
| 5. How do you interpret the results of central tendency and dispersion? | |
Ans. Interpreting central tendency results helps identify the most common or typical value in a dataset, while dispersion results indicate the extent to which values spread out or cluster around the central tendency. Together, they provide a complete picture of the data's distribution, aiding in data analysis and decision-making.
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