0
Is The variance is the mean of the sum of the squared deviation between each observation and the median?
Wiki User
∙ 6y agoWant this question answered?
Be notified when an answer is posted
Add your answer:
Earn +20 pts
Is variance based on deviations from the mean?
Variance is the squared deviation from the mean. (X bar - X data)^2
What is the variance of a standard deviation of 12.4?
Variance is std dev squared. Therefore, if std dev = 12.4, variance = 12.4^2 = 153.76.
Is the variance of a group of scores the same as the sum of the squared deviations or the average of the squared deviations?
Given a set of n scores, the variance is sum of the squared deviation divided by n or n-1. We divide by n for the population and n-1 for the sample.
What is the equation for standard deviation?
Standard deviation is equal to the square root of the variance. To arrive at this work out the mean, then subtract the mean and square the result of each number. Then work out the mean of those squared differences and take the square root of that.
What statistical measure tells you how to spread out the data are?
There are many:Range.Inter [ ] Range : where the middle part may be quartile, quintile, decile or percentile. Other options are possible but less common.Mean absolute deviation.Mean squared deviation (variance).Standard error.Standard deviation.
Is the variance of a group of scores the same as the squared standard deviation?
The standard deviation is defined as the square root of the variance, so the variance is the same as the squared standard deviation.
What is the sum of squared deviation?
The variance.
Is variance the square root of standard deviation?
No, you have it backwards, the standard deviation is the square root of the variance, so the variance is the standard deviation squared. Usually you find the variance first, as it is the average sum of squares of the distribution, and then find the standard deviation by squaring it.
1 The average of the squared deviation scores from a distribution mean?
Variance
Suppose the standard deviation is 13.1 what is the variance?
13.1 squared = 3.62
Can the standard deviation or variance be negative?
No, a standard deviation or variance does not have a negative sign. The reason for this is that the deviations from the mean are squared in the formula. Deviations are squared to get rid of signs. In Absolute mean deviation, sum of the deviations is taken ignoring the signs, but there is no justification for doing so. (deviations are not squared here)
Why standard deviation is more often used than variance?
Both variance and standard deviation are measures of dispersion or variability in a set of data. They both measure how far the observations are scattered away from the mean (or average). While computing the variance, you compute the deviation of each observation from the mean, square it and sum all of the squared deviations. This somewhat exaggerates the true picure because the numbers become large when you square them. So, we take the square root of the variance (to compensate for the excess) and this is known as the standard deviation. This is why the standard deviation is more often used than variance but the standard deviation is just the square root of the variance.
Is variance based on deviations from the mean?
Variance is the squared deviation from the mean. (X bar - X data)^2
What is the variance of a standard deviation of 12.4?
Variance is std dev squared. Therefore, if std dev = 12.4, variance = 12.4^2 = 153.76.
What word means the average of the squared deviation scores from a distribution mean?
Variance
Why variance is bigger than standard deviation?
The variance is standard deviation squared, or, in other terms, the standard deviation is the square root of the variance. In many cases, this means that the variance is bigger than the standard deviation - but not always, it depends on the specific values.
Is the variance of a group of scores the same as the sum of the squared deviations or the average of the squared deviations?
Given a set of n scores, the variance is sum of the squared deviation divided by n or n-1. We divide by n for the population and n-1 for the sample.
