answersLogoWhite

0


Best Answer

Your middle point or line for the plot (mean) would be 6.375. Then you would add/subtract 1.47 from your mean. For example, one standard deviation would equal

6.375 + 1.47 and one standard deviation from the left would be

6.375 - 1.47

User Avatar

Wiki User

14y ago
This answer is:
User Avatar

Add your answer:

Earn +20 pts
Q: Using mean of 6.375 and Standard deviation of 1.47 plot values on normal distribution?
Write your answer...
Submit
Still have questions?
magnify glass
imp
Continue Learning about Statistics

What are importance of mean and standard deviation in the use of normal distribution?

For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution


What percentage of data values of a normal distribution will fall within one standard deviation below the mean?

34.1% of the data values fall between (mean-1sd) and the mean.


How does one interpret a standard deviation which is more than the mean?

Standard deviation is a measure of the dispersion of the data. When the standard deviation is greater than the mean, a coefficient of variation is greater than one. See: http://en.wikipedia.org/wiki/Coefficient_of_variation If you assume the data is normally distributed, then the lower limit of the interval of the mean +/- one standard deviation (68% confidence interval) will be a negative value. If it is not realistic to have negative values, then the assumption of a normal distribution may be in error and you should consider other distributions. Common distributions with no negative values are gamma, log normal and exponential.


What are all the values a standard deviation can take?

The standard deviation must be greater than or equal to zero.


What makes the range less desirable than the standard deviation as a measure of dispersion?

Range can include outliers that are not normal values and can skew overall data. Most relevant values can be found within one or two standard deviations on a normal curve.

Related questions

What are importance of mean and standard deviation in the use of normal distribution?

For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution


What is standard deviation stretch?

The standard deviation stretch is used to stretch the output values using a normal distribution. The result of this stretch is similar to what is seen by the human eye.


What is the mean and standard deviations and the standard normal distribution?

Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.


What is the z value for a normal distribution?

If a random variable X has a Normal distribution with mean m and standard deviation s, then z = (X - m)/s has a Standard Normal distribution. That is, Z has a Normal distribution with mean 0 and standard deviation 1. Probabilities for a general Normal distribution are extremely difficult to obtain but values for the Standard Normal have been calculated numerically and are widely tabulated. The z-transformation is, therefore, used to evaluate probabilities for Normally distributed random variables.


A set of 1000 values has a normal distribution the mean of the data is 120 and the standard deviation is 20 how many values are within one standard deviaiton from the mean?

The Empirical Rule states that 68% of the data falls within 1 standard deviation from the mean. Since 1000 data values are given, take .68*1000 and you have 680 values are within 1 standard deviation from the mean.


A What is empirical rule?

For data sets having a normal, bell-shaped distribution, the following properties apply: About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean.


What percentage of data values of a normal distribution will fall within one standard deviation below the mean?

34.1% of the data values fall between (mean-1sd) and the mean.


The distribution of ACT scores in recent years has been roughly normal with mean 20.9 and standard deviation 4.4 The quartiles of any distribution are the values with cumulative proportions 0.25 and?

17.7 and 20.9


What is standard deviation in psychology?

Standard deviation is a statistical measure. It may be used in psychology but is not restricted to that subject. It is a measure of the spread of the distribution of values of some attribute that is being measured.


Why is the standard deviation of a distribution of means smaller than the standard deviation of the population from which it was derived?

The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.


How does one interpret a standard deviation which is more than the mean?

Standard deviation is a measure of the dispersion of the data. When the standard deviation is greater than the mean, a coefficient of variation is greater than one. See: http://en.wikipedia.org/wiki/Coefficient_of_variation If you assume the data is normally distributed, then the lower limit of the interval of the mean +/- one standard deviation (68% confidence interval) will be a negative value. If it is not realistic to have negative values, then the assumption of a normal distribution may be in error and you should consider other distributions. Common distributions with no negative values are gamma, log normal and exponential.


What is the mean and standard deviation of a distribution of T-scores?

T-scores have a mean of 50 and a standard deviation of 10. These values are fixed and do not change regardless of the distribution of T-scores.