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Common method is to find the mean and the standard deviation
of the data set and then call anything that falls more
than three standard deviations away from the mean an outlier. That
is, x is an outlier if
abs(x - mean)
--------------- > 3
std dev
This is usually called a z-test in statistics books, and the ratio
abs(x-mean)/(std dev) is abbreviated z.
Source: http://mathforum.org/library/drmath/view/52720.html
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∙ 14y ago
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Is an IQ Score of 113 a normal average score for a 14 year old Female?
Yes. IQs are normalized by age. The average IQ is 100 with a standard deviation of 15 by definition. So, a 113 falls right at the top of the average range.
Why do we repeat lab results?
In order to check that the result is correct and reliable. For measurements, it establishes the range of results we can expect and highlights any outliers.
What is standardized variables?
A variable that has been transformed by multiplication of all scores by a constant and/or by the addition of a constant to all scores. Often these constants are selected so that the transformed scores have a mean of zero and a variance (and standard deviation) of 1.0.
What does a high t statistic mean?
Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated. Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated.
Why do you always do repeat readings in science investigation?
So it is easier to work out a mean (average) and makes your results more accurate. Also you can spot outliers. But it's incredibly annoying :L
The mean and standard deviation are usually not used together because of outliers?
false
If outliers are added to a dataset how would the variance and standard deviation change?
They would both increase.
What is the relation between quartile deviation and standard deviation?
Strictly speaking, none. A quartile deviation is a quick and easy method to get a measure of the spread which takes account of only some of the data. The standard deviation is a detailed measure which uses all the data. Also, because the standard deviation uses all the observations it can be unduly influenced by any outliers in the data. On the other hand, because the quartile deviation ignores the smallest 25% and the largest 25% of of the observations, there are no outliers.
If quartile deviation is 24. find mean deviation and standard deviation?
Information is not sufficient to find mean deviation and standard deviation.
Which of the following is least affected if an extreme high outlier is added to your data mean median or standard deviation or ALL?
The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.
Is absolute mean deviation affected by outliers?
Yes.
Why we calculate standard deviation and quartile deviation?
we calculate standard deviation to find the avg of the difference of all values from mean.,
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.
How do you find the sample deviation?
You're an idiot. It's standard deviation. Google that for your answer.
How do you find graph median by using standard deviation?
You cannot because the median of a distribution is not related to its standard deviation.
Why would someone need a standard deviation calculator?
A standard deviation calculator allows the user to find the mean spread away from the mean in a statistical environment. Most users needing to find the standard deviation are in the statistics field. Usually, the data set will be given and must be typed into the calculator. The standard deviation calculator will then give the standard deviation of the data. In order to find the variance of the data, simply square the answer.
How do i find sample standard deviation from population standard deviation?
If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]
