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Measures of Central Tendency Video Lecture | Statistics for Economics - Class XI - Commerce
FAQs on Measures of Central Tendency Video Lecture - Statistics for Economics - Class XI - Commerce
| 1. What are the different measures of central tendency? |  |
Ans. The different measures of central tendency are mean, median, and mode.
- The mean is obtained by summing all the values in a dataset and dividing by the total number of values.
- The median is the middle value when the dataset is arranged in ascending or descending order.
- The mode is the value that appears most frequently in the dataset.
| 2. How can the mean be affected by outliers? | |
Ans. Outliers, which are extreme values in a dataset, can heavily influence the mean. If there are outliers with significantly higher or lower values than the rest of the dataset, the mean will be pulled towards these extreme values. This can result in a misleading representation of the average value, especially if the outliers are not representative of the overall distribution.
| 3. When is it appropriate to use the median instead of the mean? | |
Ans. The median is appropriate to use when the dataset contains outliers or is skewed. Unlike the mean, the median is not affected by extreme values. Therefore, if there are outliers present, using the median provides a more robust measure of central tendency that better represents the typical value in the dataset.
| 4. What is the significance of the mode in data analysis? | |
Ans. The mode is significant in data analysis as it represents the most frequently occurring value in a dataset. It is particularly useful for categorical or discrete data since it provides the category or value that is most common. For example, the mode can be used to identify the most popular choice in a survey or the most common color in a set of objects.
| 5. How do you interpret a dataset with multiple modes? | |
Ans. If a dataset has multiple modes, it is referred to as multimodal. This means that there are multiple values that appear with the highest frequency. In such cases, the dataset may have multiple peaks or clusters. It is important to consider the context and characteristics of the data to interpret the multiple modes correctly. For example, in a bimodal distribution, there may be two distinct groups or categories present in the data.
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