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_________ is the expected value of (x – m)2 , where m is the mean.
- a)median
- b)variance
- c)standard deviation
- d)mode
Correct answer is option 'B'. Can you explain this answer?
Most Upvoted Answer
_________ is the expected value of (x – m)2 , where m is the mea...
The expected value of (x m)2, where m is the mean, is known as the variance. It gives an idea about how spread out the data is from the mean. The formula for variance is:
Variance = (Σ(xi – m)2) / n
Where,
xi = the value of the ith observation
m = the mean of the observations
n = the number of observations
Explanation:
Mean is the arithmetic average of the observations in a dataset. It is calculated by adding up all the observations and dividing by the number of observations. The formula for mean is:
Mean = Σxi / n
Where,
xi = the value of the ith observation
n = the number of observations
Variance measures the difference between each observation and the mean. It is calculated by taking the square of the difference between each observation and the mean, adding up all the squares, and dividing by the number of observations. The formula for variance is:
Variance = (Σ(xi – m)2) / n
Where,
xi = the value of the ith observation
m = the mean of the observations
n = the number of observations
The variance helps to determine how spread out the data is from the mean. A large variance indicates that the data is widely spread out from the mean, while a small variance indicates that the data is tightly clustered around the mean. The variance is always a positive number or zero, and it is measured in squared units.
Conclusion:
In conclusion, the expected value of (x m)2, where m is the mean, is known as the variance. It gives an idea about how spread out the data is from the mean. The variance is calculated by taking the square of the difference between each observation and the mean, adding up all the squares, and dividing by the number of observations.
Variance = (Σ(xi – m)2) / n
Where,
xi = the value of the ith observation
m = the mean of the observations
n = the number of observations
Explanation:
Mean is the arithmetic average of the observations in a dataset. It is calculated by adding up all the observations and dividing by the number of observations. The formula for mean is:
Mean = Σxi / n
Where,
xi = the value of the ith observation
n = the number of observations
Variance measures the difference between each observation and the mean. It is calculated by taking the square of the difference between each observation and the mean, adding up all the squares, and dividing by the number of observations. The formula for variance is:
Variance = (Σ(xi – m)2) / n
Where,
xi = the value of the ith observation
m = the mean of the observations
n = the number of observations
The variance helps to determine how spread out the data is from the mean. A large variance indicates that the data is widely spread out from the mean, while a small variance indicates that the data is tightly clustered around the mean. The variance is always a positive number or zero, and it is measured in squared units.
Conclusion:
In conclusion, the expected value of (x m)2, where m is the mean, is known as the variance. It gives an idea about how spread out the data is from the mean. The variance is calculated by taking the square of the difference between each observation and the mean, adding up all the squares, and dividing by the number of observations.
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_________ is the expected value of (x – m)2 , where m is the mea...
B) variance
because variance of random variable x is defined as the arithmetic mean of the square of deviations taken about arithmetic mean
because variance of random variable x is defined as the arithmetic mean of the square of deviations taken about arithmetic mean
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_________ is the expected value of (x – m)2 , where m is the mean.a)medianb)variancec)standard deviationd)modeCorrect answer is option 'B'. Can you explain this answer?
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