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Q: What percent of the observations lie within plus and minus 1.75 standard deviations of the meanAccording to Chebyshev's Theorem?

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The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95

When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as

They are observations with a low likelihood of occurrence. They may be called outliers but there is no agreed definition for outliers.

How many standard deviations is 16.50 from the mean?

You can't average means with standard deviations. What are you trying to do with the two sets of data?

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95%

about 68%

The standard deviation of a set of data is a measure of the spread of the observations. It is the square root of the mean squared deviations from the mean of the data.

Usually they would be observations with very low probabilities of occurrence.

The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95

When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as

They are observations with a low likelihood of occurrence. They may be called outliers but there is no agreed definition for outliers.

How many standard deviations is 16.50 from the mean?

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.

The sum of standard deviations from the mean is the error.

It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.

No. The curve in a normal distribution goes on out to plus and minus infinity. You might never see any observations out there, but if you were to make an infinite number of observations, you theoretically would.