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How many standard deviations above or below the mean should a score be to establish significance?
Wiki User
∙ 12y ago
It depends on the significance level required. And that, in turn, will depend on the cost of making the wrong decision. For ordinary use, a 95% significance level will require 1.96 sd
Wiki User
∙ 12y ago
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If average height for women is normally distributed with a mean of 65 inches and a standard deviation of 2.5 inches then approximately 95 percent of all women should be between what and what inches?
A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.
When computing Standard deviation should you eliminate the extreme values?
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
How do you solve this problem. You drive to work. Drive time mean 30 min standard deviation 4 min. Workday begins at 9am. What time should you leave so that probability on time is 95 percent?
if standard deviation is 4 minutes 95% probability is about 2 standard deviations (actually 1.96) so you would need to allow 30 + 8 = 38 minutes
The Sortino Ratio?
I've written before about the Sharpe Ratio, a measure of risk-adjusted returns for an asset or portfolio. The Sharpe ratio functions by dividing the difference between the returns of that asset or portfolio and the risk-free rate of return by the standard deviation of the returns from their mean. So it gives you an idea of the level of risk assumed to earn each marginal unit of return. The problem with using the Sharpe Ratio is that it assumes that all deviations from the mean are risky, and therefore bad. But often those deviations are upward movements. Why should an investment strategy by graded so sharply by the Sharpe Ratio for good performance? In the real world, investors don't usually mind upside deviations from the mean. Why would they? These were the questions on the mind of Frank Sortino when he developed what has been dubbed the Sortino Ratio. The ratio that bears his name is a modification of the Sharpe Ratio that only takes into account negative deviations and counts them as risk. To me, it always made a lot more sense not to include upside volatility from the equation because I rather like to see some upside volatility in my portfolios. With the Sortino Ratio only downside volatility is used as the denominator in the equation. So the way you calculate it is to divide the difference between the expected rate of return and the risk-free rate by the standard deviation of negative asset returns. (It can be a bit tricky the first time you try to do it. The positive deviations are set to values of zero during the standard deviation calculation in order to calculate downside deviation.) By using the Sortino Ratio instead of the Sharpe Ratio you’re not penalizing the investment manager or strategy for any upside volatility in the portfolio. And doesn’t that make a whole lot more sense?
How many outliers can a data set have?
There is no agreed definition of an outlier and consequently, there is no simple answer to the question. The number of outliers will depend on the criterion used to identify them. If you have observations from a normal distribution, you should expect around 1 in 22 observations to be more than 2 standard deviations from the mean, and about 1 in 370 more than 3 sd away. You will have more outliers if the distribution is non-normal - particularly if it is skewed.
How do you multiply standard deviations?
Multiply them as you would any two numbers. However, you should note that the standard deviation of a product of two variables is not the product of their standard deviations. That is, SD(XY) ≠SD(X)*SD(Y)
What is the measures that fall beyond three standard deviations of the mean called?
You may be referring to the statistical term 'outlier(s)'. Also, there is a rule in statistics called the '68-95-99 Rule'. It states that in a normally distributed dataset approximately 68% of the observations will be within plus/minus one standard deviation of the mean, 95% within plus/minus two standard deviations, and 99% within plus/minus three standard deviations. So if your data follow the classic bell-shaped curve, roughly 1% of the measures should fall beyond three standard deviations of the mean.
Is standard deviation the arithmetic mean of the squared deviations from the means?
No. Standard deviation is the square root of the mean of the squared deviations from the mean. Also, if the mean of the data is determined by the same process as the deviation from the mean, then you loose one degree of freedom, and the divisor in the calculation should be N-1, instead of just N.
If average height for women is normally distributed with a mean of 65 inches and a standard deviation of 2.5 inches then approximately 95 percent of all women should be between what and what inches?
A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.
How is Pluto's discovery similar to that of Neptune?
Both Pluto and Neptune were discovered by observing deviations in the movement of other planets from what they were calculated to be, and from that calculating where something else should be that was causing those deviations.
When computing Standard deviation should you eliminate the extreme values?
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
How do you solve this problem. You drive to work. Drive time mean 30 min standard deviation 4 min. Workday begins at 9am. What time should you leave so that probability on time is 95 percent?
if standard deviation is 4 minutes 95% probability is about 2 standard deviations (actually 1.96) so you would need to allow 30 + 8 = 38 minutes
Can the non custodial father file for a child support order for himself?
Yes. A non-custodial parent can file a petition to establish his parental rights. He can petition to establish his paternity, to establish custody and visitation and to establish a child support order. He should visit the court and ask to speak with an advocate in the family court. If possible he should consult with an attorney.Yes. A non-custodial parent can file a petition to establish his parental rights. He can petition to establish his paternity, to establish custody and visitation and to establish a child support order. He should visit the court and ask to speak with an advocate in the family court. If possible he should consult with an attorney.Yes. A non-custodial parent can file a petition to establish his parental rights. He can petition to establish his paternity, to establish custody and visitation and to establish a child support order. He should visit the court and ask to speak with an advocate in the family court. If possible he should consult with an attorney.Yes. A non-custodial parent can file a petition to establish his parental rights. He can petition to establish his paternity, to establish custody and visitation and to establish a child support order. He should visit the court and ask to speak with an advocate in the family court. If possible he should consult with an attorney.
What two kinds of time did Sir Sandford Fleming want to establish?
He proposed in 1879 that there should be Universal Standard Time covering the whole world linked back to the time at Greenwich.
Describe the properties of a normal distribution?
A normal distribution is symmetric and when looked at on a graph, the graph looks like a bell shaped curve. Approximately 95 percent of its values should lie within two standard deviations of the mean. Frequency of the data lies mostly in the middle of the curve.
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