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Mean of the squares of the deviations from mean is called the:
- a)Mode
- b)Standard deviation
- c)Variance
- d)Quartile deviation
Correct answer is option 'C'. Can you explain this answer?
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Mean of the squares of the deviations from mean is called the:a)Modeb)...
Squared deviations from the mean Squared deviations from the mean (SDM) are involved in various calculations. In probability theory and statistics, the definition of variance is either the expected value of the SDM (when considering a theoretical distribution) or its average value (for actual experimental data).
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Mean of the squares of the deviations from mean is called the:a)Modeb)...
Mean of the squares of the deviations from mean:
One way to measure the spread of data in a dataset is by calculating the variance, which is the mean of the squares of the deviations from the mean. This statistic gives us an idea of how much the values in a dataset differ from the average value.
Explanation:
- Variance: The variance is calculated by taking the difference between each data point and the mean, squaring that difference, summing up all the squared differences, and then dividing by the total number of data points.
- Mean of the squares of the deviations: This is essentially the average of the squared differences between each data point and the mean of the dataset.
Significance:
- The variance gives us an indication of how spread out the values in a dataset are.
- A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that the data points are closer to the mean.
Relation to other statistical measures:
- Variance is related to the standard deviation, which is the square root of the variance.
- While the variance gives us an idea of the spread of data in squared units, the standard deviation provides a measure of spread in the original units of the data.
In conclusion, the mean of the squares of the deviations from the mean is known as the variance, and it is an important measure of spread in a dataset. It helps us understand how much the values in the dataset deviate from the average value.
One way to measure the spread of data in a dataset is by calculating the variance, which is the mean of the squares of the deviations from the mean. This statistic gives us an idea of how much the values in a dataset differ from the average value.
Explanation:
- Variance: The variance is calculated by taking the difference between each data point and the mean, squaring that difference, summing up all the squared differences, and then dividing by the total number of data points.
- Mean of the squares of the deviations: This is essentially the average of the squared differences between each data point and the mean of the dataset.
Significance:
- The variance gives us an indication of how spread out the values in a dataset are.
- A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that the data points are closer to the mean.
Relation to other statistical measures:
- Variance is related to the standard deviation, which is the square root of the variance.
- While the variance gives us an idea of the spread of data in squared units, the standard deviation provides a measure of spread in the original units of the data.
In conclusion, the mean of the squares of the deviations from the mean is known as the variance, and it is an important measure of spread in a dataset. It helps us understand how much the values in the dataset deviate from the average value.
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Community Answer
Mean of the squares of the deviations from mean is called the:a)Modeb)...
Variance is the mean of the squares of the deviations from the mean. Variance is the square of standard deviation. Therefore any unit of a given set is converted into squares at the time of calculating the variance.
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Mean of the squares of the deviations from mean is called the:a)Modeb)Standard deviationc)Varianced)Quartile deviationCorrect answer is option 'C'. Can you explain this answer?
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