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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.
What is the area within the normal curve between -1SD and plus 1 SD?
The area within the normal curve between -1 standard deviation (SD) and +1 SD is approximately 68%. This means that about 68% of the data falls within one standard deviation of the mean in a normal distribution.
Scores on the sat exam approximate a normal distribution with µ equals 500 and sd equals 100 use this distribution to determine the percentage of sat scores that fall above 600?
The answer is about 16% Using the z-score formula(z = (x-u)/sd) the z score is 1. This means that we want the percentage above 1 standard deviation. We know from the 68-95-99.7 rule that 68 percent of all the data fall between -1 and 1 standard deviation so there must be about 16% that falls above 1 standard deviation.
What percentage of the area of a normal standard distribution falls between z and z?
Zero.
Why is it that only one normal distribution table is needed to find any probability under the normal curve?
Anything that is normally distributed has certain properties. One is that the bulk of scores will be near the mean and the farther from the mean you are, the less common the score. Specifically, about 68% of anything that is normally distributed falls within one standard deviation of the mean. That means that 68% of IQ scores fall between 85 and 115 (the mean being 100 and standard deviation being 15) AND 68% of adult male heights fall between 65 and 75 inches (the mean being 70 and I am estimating a standard deviation of 5). Basically, even though the means and standard deviations change, something that is normally distributed will keep these probabilities (relative to the mean and standard deviation). By standardizing these numbers (changing the mean to 0 and the standard deviation to 1) we can use one table to find the probabilities for anything that is normally distributed.
What percentage of the data falls outside 2 standard deviation of the mean?
4.55% falls outside the mean at 2 standard deviation
What percentage of the data falls outside 1 standard deviation of the mean?
One standard deviation for one side will be 34% of data. So within 1 std. dev. to both sides will be 68% (approximately) .the data falls outside 1 standard deviation of the mean will be 1.00 - 0.68 = 0.32 (32 %)
What is the percentage of population with an IQ of 128?
Approximately 6.68% of the population falls within one standard deviation above the mean IQ score of 100, which includes an IQ of 128.
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.
What percentage of the area falls below the mean?
The area between the mean and 1 standard deviation above or below the mean is about 0.3413 or 34.13%
What is the area within the normal curve between -1SD and plus 1 SD?
The area within the normal curve between -1 standard deviation (SD) and +1 SD is approximately 68%. This means that about 68% of the data falls within one standard deviation of the mean in a normal distribution.
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 percent of a normal population is within 2 standard deviations of the mean?
In a normal distribution, approximately 95% of the population falls within 2 standard deviations of the mean. This is known as the 95% rule or the empirical rule. The empirical rule states that within one standard deviation of the mean, about 68% of the population falls, and within two standard deviations, about 95% of the population falls.
What percentage of the country falls within Norway?
100% of Norway falls within Norway
Scores on the sat exam approximate a normal distribution with µ equals 500 and sd equals 100 use this distribution to determine the percentage of sat scores that fall above 600?
The answer is about 16% Using the z-score formula(z = (x-u)/sd) the z score is 1. This means that we want the percentage above 1 standard deviation. We know from the 68-95-99.7 rule that 68 percent of all the data fall between -1 and 1 standard deviation so there must be about 16% that falls above 1 standard deviation.
What percentage of the area of a normal standard distribution falls between z and z?
Zero.
Why is it that only one normal distribution table is needed to find any probability under the normal curve?
Anything that is normally distributed has certain properties. One is that the bulk of scores will be near the mean and the farther from the mean you are, the less common the score. Specifically, about 68% of anything that is normally distributed falls within one standard deviation of the mean. That means that 68% of IQ scores fall between 85 and 115 (the mean being 100 and standard deviation being 15) AND 68% of adult male heights fall between 65 and 75 inches (the mean being 70 and I am estimating a standard deviation of 5). Basically, even though the means and standard deviations change, something that is normally distributed will keep these probabilities (relative to the mean and standard deviation). By standardizing these numbers (changing the mean to 0 and the standard deviation to 1) we can use one table to find the probabilities for anything that is normally distributed.
