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What else can I help you with?
Is The variance is the mean of the sum of the squared deviation between each observation and the median?
No, the variance is not defined as the mean of the sum of the squared deviations from the median; rather, it is the mean of the squared deviations from the mean of the dataset. Variance measures how much the data points differ from the mean, while the median is a measure of central tendency that may not accurately reflect the spread of the data in the same way. Though both concepts involve deviations, they use different points of reference for their calculations.
When computing the sample variance the sum of squared deviations about the mean is used for what reason?
You want some measure of how the observations are spread about the mean. If you used the deviations their sum would be zero which would provide no useful information. You could use absolute deviations instead. The sum of squared deviations turns out to have some useful statistical properties including a relatively simple way of calculating it. For example, the Gaussian (or Normal) distribution is completely defined by its mean and variance.
Why don't we measure spread about the mean by simply averaging the deviations of individual data values from their mean?
Averaging the deviations of individual data values from their mean would always result in zero, since the mean is the point at which the sum of deviations is balanced. This occurs because positive and negative deviations cancel each other out. Instead, measures like variance and standard deviation are used, which square the deviations to ensure all values contribute positively, providing a meaningful representation of spread around the mean.
Why do you square the deviations to get the variance and then take the reverse action of taking the square root of the variance to return the variance to sigma?
because of two things- a) both positive and negative deviations mean something about the general variability of the data to the analyst, if you added them they'd cancel out, but squaring them results in positive numbers that add up. b) a few larger deviations are much more significant than the many little ones, and squaring them gives them more weight. Sigma, the square root of the variance, is a good pointer to how far away from the mean you are likely to be if you choose a datum at random. the probability of being such a number of sigmas away is easily looked up.
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.
The sum of the deviations about the mean always equals what?
The sum of total deviations about the mean is the total variance. * * * * * No it is not - that is the sum of their SQUARES. The sum of the deviations is always zero.
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)
Is The variance is the mean of the sum of the squared deviation between each observation and the median?
No, the variance is not defined as the mean of the sum of the squared deviations from the median; rather, it is the mean of the squared deviations from the mean of the dataset. Variance measures how much the data points differ from the mean, while the median is a measure of central tendency that may not accurately reflect the spread of the data in the same way. Though both concepts involve deviations, they use different points of reference for their calculations.
When computing the sample variance the sum of squared deviations about the mean is used for what reason?
You want some measure of how the observations are spread about the mean. If you used the deviations their sum would be zero which would provide no useful information. You could use absolute deviations instead. The sum of squared deviations turns out to have some useful statistical properties including a relatively simple way of calculating it. For example, the Gaussian (or Normal) distribution is completely defined by its mean and variance.
What is the variance of of the scores 3 4 8 and 9?
sum of scores: 24 mean of scores : 24/4 = 6 squared deviations from the mean: 9, 4,4,9 sum of these: 26 sample variance: 26/4 = 6.5
How do you find the variance 415 10 8 33 25 36 66 63 50 64 49 40?
First mean is calculated.Then calculate deviations from the mean.Then the deviations are squared.Then the squared deviations are summed up.Finally this sum is divided by number of items for which the variance is being calculated. For a population, it is by the number of values, in this case 12. If it is a sample, then we divide by one less, which is 11,For these figures, the variance for the population is 11069.24306. If it is a sample, it is 12075.53788 as the result.
Why don't we measure spread about the mean by simply averaging the deviations of individual data values from their mean?
Averaging the deviations of individual data values from their mean would always result in zero, since the mean is the point at which the sum of deviations is balanced. This occurs because positive and negative deviations cancel each other out. Instead, measures like variance and standard deviation are used, which square the deviations to ensure all values contribute positively, providing a meaningful representation of spread around the mean.
Why do you square the deviations to get the variance and then take the reverse action of taking the square root of the variance to return the variance to sigma?
because of two things- a) both positive and negative deviations mean something about the general variability of the data to the analyst, if you added them they'd cancel out, but squaring them results in positive numbers that add up. b) a few larger deviations are much more significant than the many little ones, and squaring them gives them more weight. Sigma, the square root of the variance, is a good pointer to how far away from the mean you are likely to be if you choose a datum at random. the probability of being such a number of sigmas away is easily looked up.
Why do you divide by instead of when calculating the sample variance?
Usually the sum of squared deviations from the mean is divided by n-1, where n is the number of observations in the sample.
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.
How do you calculate variances in a data set?
To calculate the variance of a data set, first determine the mean (average) of the data. Then, subtract the mean from each data point to find the deviation of each point, square these deviations, and sum them up. Finally, divide this total by the number of data points (for population variance) or by the number of data points minus one (for sample variance) to obtain the variance. This gives you a measure of how spread out the data points are from the mean.
What is the mean and variance of throwing unbiased dice?
When throwing a single unbiased six-sided die, the mean (expected value) is calculated as the average of the outcomes: (1 + 2 + 3 + 4 + 5 + 6) / 6 = 3.5. The variance measures the spread of the outcomes around the mean, which is calculated as the average of the squared deviations from the mean: the variance for a single die is 2.9167 (or 35/12). For multiple dice, the mean is the number of dice times 3.5, and the variance is the number of dice times 2.9167.
