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It refers to some variable whose distribution has got some mean and the measure of its spread is a standradr deviation of 17 cm. If the distribution is Normal (or approximately so), then the interval from the mean measure minus 17 cm to the mean plus 17 cm will contain approx 68% of the total number of observations.
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Why is the standard deviation one?
The standard deviation provides in indication of what proportion of the entire distribution of the sample falls within a certain distance from the mean or average for that sample. If your data falls on a normal (or bell shaped) distribution, a SD of 1 indicates that about 68% of your data points (scores or whatever else) fall within 1 point (plus or minus) of the average (mean) of the data, and 95% fall within 2 points.
What is the proportion of the total area under the normal curve within plus and minus two standard deviations of the mean?
95
What is a plus and a plus if a plus and a minus is a minus?
A plus + a plus is a plus.
If n equals 108 and p equals 0.24 find the minimum usual value with mean of -2 std deviation and maximum usual value of mean plus 2 std deviation?
5 7
What is negative 6 divided by 3?
2
The mean plus or minus the standard deviation for a normal distribution provides a probability range of what percent?
in a normal distribution, the mean plus or minus one standard deviation covers 68.2% of the data. If you use two standard deviations, then you will cover approx. 95.5%, and three will earn you 99.7% coverage
What percentage of observations of a normal distribution is represented by the mean plus or minus 1.96 standard deviation?
It is 95%
What is the 68-95-99.7 rule?
The 68-95-99.7 rule, or empirical rule, says this:for a normal distribution almost all values lie within 3 standard deviations of the mean.this means that approximately 68% of the values lie within 1 standard deviation of the mean (or between the mean minus 1 times the standard deviation, and the mean plus 1 times the standard deviation). In statistical notation, this is represented as: μ ± σ.And approximately 95% of the values lie within 2 standard deviations of the mean (or between the mean minus 2 times the standard deviation, and the mean plus 2 times the standard deviation). The statistical notation for this is: μ ± 2σ.Almost all (actually, 99.7%) of the values lie within 3 standard deviations of the mean (or between the mean minus 3 times the standard deviation and the mean plus 3 times the standard deviation). Statisticians use the following notation to represent this: μ ± 3σ.(www.wikipedia.org)
What percentage of the normally distributed population lies within the plus or minus one standard deviation of the population mean?
68.2%
What does it mean when the standard deviation is 1?
A standard deviation is a statistical measure of the variation there in a population or group. A standard deviation of 1 means that 68% of the members of the population are withing plus or minus the value of the standard deviation from the average. For example: assume the average height of men is 5 feet 9 inches, and the standard deviation is three inches. Then 68% of all men are between 5' 6" and 6' which is 5'9" plus or minus 3 inches. [Note: this is only to illustrate and is not intended to be a real/correct statistic of men's heights.]
How much is 84 percentile equals mean plus 1 standard deviation or mean plus 1.4 standard deviation. Can you give me reference also please?
The cumulative probability up to the mean plus 1 standard deviation for a Normal distribution - not any distribution - is 84%. The reference is any table (or on-line version) of z-scores for the standard normal distribution.
What area within one standard deviation between and mu and minus and sigma and and mu plus and sigma?
The answer will depend on the distribution of the variable.
What is the reference point of a standard deviation?
The mean.The mean.The mean.The mean.
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.
What is the approximate percentage score of less than 140 using the 68-95-99.7 rule if a set of test scores is normally distributed with a mean of 100 and a standard deviation of 20?
The 68-95-99.7 rule states that in a normally distributed set of data, approximately 68% of all observations lie within one standard deviation either side of the mean, 95% lie within two standard deviations and 99.7% lie within three standard deviations.Or looking at it cumulatively:0.15% of the data lie below the mean minus three standard deviations2.5% of the data lie below the mean minus two standard deviations16% of the data lie below the mean minus one standard deviation50 % of the data lie below the mean84 % of the data lie below the mean plus one standard deviation97.5% of the data lie below the mean plus two standard deviations99.85% of the data lie below the mean plus three standard deviationsA normally distributed set of data with mean 100 and standard deviation of 20 means that a score of 140 lies two standard deviations above the mean. Hence approximately 97.5% of all observations are less than 140.
What percentage of observations of a normal distribution is reprented by the mean plus or minus 1.96 standard deviations?
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
How is the standard deviation and sample variation interpreted?
Both are measures of the spread of the data around a mean or average. In fact the standard deviation == sqrt(variance). The standard deviation (SD) is defined to be the square root of the sample variance (V). EX: Some value X = M +/- 1 SD indicates that there is roughly a 2/3 probability that the value will fall randomly within the interval from minus one SD up to plus one SD around the mean M.
