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This means that the set of data is clustered really close to the mean/average. Your data set likely has a small range (highest value - lowest value).

In other words, if the average is 6.3, and the standard deviation is 0.7, this means that each individual piece of data, on average, is different from the mean by 0.7. Each piece of data deviates from the mean by an average (standard) of 0.7; hence standard deviation!

By definition, 66% of all data is 1 standard deviation from the mean, so 66% of the data in this example would be between the values of 5.6 and 7.0.

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Q: What can be said about a set of data when its standard deviation is small but not zero?
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Is the higher the standard deviation the greater the variation?

Yes. Since the standard deviation is defined as the square root of the variance, it can be said that the higher the standard deviation, the higher the variance.


What are the main qualities of a good estimator?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.


What are the quality of a good estimator?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.


How are bar graphs and histograms similar?

bar graphs and histograms similar because In bar graphs are usually used to display "categorical data", that is data that fits into categories. For example suppose that I offered to buy donuts for six people and three said they wanted chocolate covered, 2 said plain and one said with icing sugar. I would present this in a bar garph and Histograms on the other hand are usually used to present "continuous data", that is data that represents measured quantity where, at least in theory, the numbers can take on any value in a certain range. A good example is weight. If you measure the weights of a group of adults you might get and numbers between say 90 pounds and 240 pounds. We usually report our weights as pounds or to the nearest half pound but we might do so to the nearest tenth of a pound or however acurate the scale is. The data would then be collected into categories to present a histogram


In an opinion poll 25 percent of a random sample of 200 people said that they were strongly opposed to having a state lottery what is the standard error of the sample proportion?

It is 6.1, approx.

Related questions

Is the higher the standard deviation the greater the variation?

Yes. Since the standard deviation is defined as the square root of the variance, it can be said that the higher the standard deviation, the higher the variance.


Is the standard deviation dependent on the mean?

Yes. Consider the definition of the standard deviation. It is the square root of the variance from the mean. As a result, it can be said that the standard deviation is dependent on the mean.


Is it possible to have a standard deviation of less than one?

Anand Mehta said yes and this is correct. You will get a SD, for example, if all of the data points are less than one, or if the data points are very close together and there is not much spread in the data..


What is the purpose of mean absolute deviation?

Various answers to this question are possible.The mean absolute deviation (MAD) is a measure of the dispersion or spread of a sample or a population. So one of its purposes is as a measure.As such it's an alternative to the standard deviation that is said to be more robust in the sense that the sample MAD can be used to provide more accurate estimates of the population dispersion because it is less sensitive to outliers.Beyond this, some distributions that have no standard deviations do have MADs; for example, the Cauchy. This means that the dispersions of virtually all distributions can be compared in terms of their MADs.Please see the link.


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hahahahah you said 420!


How many percent of the world is Judaism?

A very small amount. The most recent data I could find was from 1997, which said it was 0.25%.


What is qualititive research?

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What is the meaning of mean absolute deviatation?

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What is gap in math?

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What is a group of precise measurements?

A group of precise measurements are a group of repetitive measurements that are very close together. Ie the standard deviation between the measurements is small. Not to be confused with a accurate measurement! Think about it like this, if you measure a piece of wood 5 times and each time you get an identical answer then the measurement are said to be precise. If however if turns out that despite measuring the length 5 times and getting the same answer you discover that the length is significantly off from the "true" answer, then you were inaccurate!


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Mother Theresa Said These Words :)