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Find the standard deviation of the binomial distribution for which n 1000 and p 0.94?
Wiki User
∙ 9y ago
The standard deviation is sqrt[n*p*(1-p)] = sqrt(1000*0.94*0.06) = 7.51 approx.
Wiki User
∙ 9y ago
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What is the value of the binomial coefficient for 500 choose 100?
1000
What is the difference between Chebyshevs inequality and empirical rule in terms of skewness?
Chebyshev's inequality: The fraction of any data set lying within K standard deviations is always at least 1-1/K^2 where K is any positive number greater than 1. It does not assume that any distribution. Now, there is the empirical rule of bell shaped curves or the 68-95-99.7 rule, which states that for a bell shaped curve: 68% of all values should fall within 1 standard deviation, 95% of all values should fall within 2 standard deviations and 99.7% of all values should fall within 3 standard deviation. If we suspect that our data is not bell shaped, but right or left skewed, the above rule can not be applied. I note that one test of skewness is Pearson's index of skewness, I= 3(mean of data - median of data)/(std deviation) If I is greater or equal to 1000 or I is less than 1, the data can be considered significantly skewed. I hope this answers your question. I used the textbook Elementary Statistics by Triola for the information on Pearson's index. If this answer is insufficient, please resubmit and be a bit more definitive on what you mean by empirical rule.
According to the normal probability distribution 95 percent of the values of a normal random variable are contained w in plus or minus how many standard deviations from the mean Why is that important?
Approximately 2 standard deviations (1.96, actually) from the mean. That is important to know that if one has a sample of 1000 values, if one selects a threshold at +/- 2 standard deviations from the mean, then one expects to see about 25 values exceeding those thresholds (on each side of the mean)
How many leaves fit in a leaf bag?
My kids found out that 40,000 leaves fit in one of the tall paper bags. They did this by first making three piles of 100 leaves which they counted, then making 10 piles of 100 leaves to make 1000. Then they made 9 more piles of that size to see how much 10,000 was. Then they made 3-4 of those piles and started filling a bag. They got 4 of the piles in so that's how we get 40,000. Discounting systematic errors, I would expect the standard deviation to be about 20% based on propagating the standard deviation of the initial three piles of 100. I would say the worst case is 100% error or a factor of 2.
How many peaks would you expect for the distribution of the results of rolling a fair die 1000 times?
With 1000 rolls of a die, and each number having a probability of 1/6, I would not expect any peaks.
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.
How is it possible to convert normal distributions to the standard normal distribution?
use this link http://www.ltcconline.net/greenl/Courses/201/probdist/zScore.htm Say you start with 1000 observations from a standard normal distribution. Then the mean is 0 and the standard deviation is 1, ignoring sample error. If you multiply every observation by Beta and add Alpha, then the new results will have a mean of Alpha and a standard deviation of Beta. Or, do the reverse. Start with a normal distribution with mean Alpha and standard deviation Beta. Subtract Alpha from all observations and divide by Beta and you wind up with the standard normal distribution.
What determines the standard deviation to be high?
Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.
Is it acceptable if standard deviation is 8?
It may or may not be acceptable. If the mean is 12, then no it is not acceptable. If the mean is 1000, then it may be acceptable depending on the criteria given.
What is the value of the binomial coefficient for 500 choose 100?
1000
Can normal distridution apply on discrete data?
Yes, If you have a large data set, you can approximate the discrete data by Normal distribution (which is continuous). An example would be, "A coin is tossed 1000 times. What is the probability of rolling between 300 and 400 heads?" This problem, usually solved by Binomial distribution (which is a discrete distribution), is very difficult to solve because of the large data set and can be approximated by the Normal distribution.
If 1000 students take a test that has a mean of 40 minutes a standard deviation of 8 minutes and is normally distributed how many would you expect would finish in less than 40 minutes?
The expected number is 500.
What is variability of a set of numerical data?
Variability is determined by the how numbers are distributed across the set of numbers. There are several ways of measuring this the most common is standard deviation. To find standard deviation you first find the average of the set by adding them all up and dividing by the amount of numbers in the set. Then you find the square of each number in the set minus the average. You add all these values up, multiply them by 1/the number of items in the set, and take the square root. As an example the set {2,5,3,6} has much less variability as measured by the standard deviation than {2000,-1000,-500,484} even though they both have the same average. The firsts average is (2+5+3+6)/4 or 4. The standard deviation is the square root of(((2-4)^2+(5-4)^2+(3-4)^2+(6-4)^2)*1/4) or about 1.58113883. The standard deviation of the second set that has the same average as the first is the square root of (((2000-4)^2+(-1000-4)^2+(-500-4)^2+(484-4)^2)*1/4) or 1170.09059.
In standard conversion what is 1000 by 1000 equal?
no such thing
How do you write 17 thousandths into standard form?
17 thousandths, in standard form, is 17/1000, or 17 over 1000.
What is the binomial factor in the factorization of m15 minus 1000?
(m^5 - 10)(m^10 + 10m^5 + 100)
How do you write 7 by 1000 in standard form?
7/1000 = 0.007
