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What are the advantages and disadvantages of using the chebyshev theorem in statistics?
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The mean of fifty sales receipts is 58.35 and the standard deviation is 10.38 Using Chebyshev's theorem determine how many sales receipts were between 37.59 and 79.11 Points 2?
47.72 I guess
What is the Test for normal distribution?
Normal Distribution is a key to Statistics. It is a limiting case of Binomial and Poisson distribution also. Central limit theorem converts random variable to normal random variable. Also central limit theorem tells us whether data items from a sample space lies in an interval at 1%, 5%, 10% siginificane level.
Statement of sampling theorem?
sampling theorem is used to know about sample signal.
Derivation of sampling theorem?
sam. theorm
What does the central limit theorem say about the shape of the sampling distribution of?
The central limit theorem can be used to determine the shape of a sampling distribution in which of the following scenarios?
Can you always find a prime number between any number and its double?
Yes, if you take the range to be inclusive, it even works for 1, since 2 is prime. The theorem related to this question is called Bertrand's Postulate, or Chebyshev's Theorem, or the Bertrand-Chebyshev theorem.
What is Chebyshev's theorem?
Ok so basically the chebyshev's theorem states that 75% of your data will lie within 2 standard deviations of the mean and that 89% of your data will lie within 3 standard deviations of the mean. And I believe that this theorem is much more precise than the Empirical Rule, which assumes normality and can be off. I am currently taking a stats. class if I didn't help or my wording is unclear please let me know. Also any information here was obtained or learned from the book called Elementary Statistics by Mario F. Triola
State the main reason for using the empirical rule rather than chebyshevs theorem?
The empirical rule can only be used for a normal distribution, so I will assume you are referring to a normal distribution. Chebyshev's theorem can be used for any distribution. The empirical rule is more accurate than Chebyshev's theorem for a normal distribution. For 2 standard deviations (sd) from the mean, the empirical rule says 95% of the data are within that, and Chebyshev's theorem says 1 - 1/2^2 = 1 - 1/4 = 3/4 or 75% of the data are within that. From the standard normal distribution chart, the answer for 2 sd from the mean is 95.44% So, as you can see the empirical rule is more accurate.
The mean of fifty sales receipts is 58.35 and the standard deviation is 10.38 Using Chebyshev's theorem determine how many sales receipts were between 37.59 and 79.11 Points 2?
47.72 I guess
What are the disadvantages of Pick's Theorem?
Pick's theorem can't use for non-convex polygons. It needs at least 3 terms to define an area of a polygon.
What is chebychev's rule?
Chebyshev's rule, also known as Chebyshev's inequality, is a statistical theorem that describes the proportion of values that fall within a certain number of standard deviations from the mean in any distribution. It states that for any set of data, regardless of the shape of the distribution, at least (1 - 1/k^2) where k is greater than 1, of the data values will fall within k standard deviations of the mean.
What are the disadvantages of using the thevenin theorem?
Thevenin's theorem is only valid for linear and bilateral networks.Practically, linearity of any circuit is over a certain range.Hence it is only valid for certain range.
Statistic question help?
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
What are the advantages and disadvantages of maximum power transfer theorem?
The Maximum Power Transfer Theorem is not so much a means of analysis as it is an aid to system design. The maximum amount of power will be dissipated by a load resistance when that load resistance is equal to the Thevenin/Norton resistance of the network supplying the power.
What are the differences between the Emperical Rule and Chebyshev's Theorem?
The Empirical Rule applies solely to the NORMAL distribution, while Chebyshev's Theorem (Chebyshev's Inequality, Tchebysheff's Inequality, Bienaymé-Chebyshev Inequality) deals with ALL (well, rather, REAL-WORLD) distributions. The Empirical Rule is stronger than Chebyshev's Inequality, but applies to fewer cases. The Empirical Rule: - Applies to normal distributions. - About 68% of the values lie within one standard deviation of the mean. - About 95% of the values lie within two standard deviations of the mean. - About 99.7% of the values lie within three standard deviations of the mean. - For more precise values or values for another interval, use a normalcdf function on a calculator or integrate e^(-(x - mu)^2/(2*(sigma^2))) / (sigma*sqrt(2*pi)) along the desired interval (where mu is the population mean and sigma is the population standard deviation). Chebyshev's Theorem/Inequality: - Applies to all (real-world) distributions. - No more than 1/(k^2) of the values are more than k standard deviations away from the mean. This yields the following in comparison to the Empirical Rule: - No more than [all] of the values are more than 1 standard deviation away from the mean. - No more than 1/4 of the values are more than 2 standard deviations away from the mean. - No more than 1/9 of the values are more than 3 standard deviations away from the mean. - This is weaker than the Empirical Rule for the case of the normal distribution, but can be applied to all (real-world) distributions. For example, for a normal distribution, Chebyshev's Inequality states that at most 1/4 of the values are beyond 2 standard deviations from the mean, which means that at least 75% are within 2 standard deviations of the mean. The Empirical Rule makes the much stronger statement that about 95% of the values are within 2 standard deviations of the mean. However, for a distribution that has significant skew or other attributes that do not match the normal distribution, one can use Chebyshev's Inequality, but not the Empirical Rule. - Chebyshev's Inequality is a "fall-back" for distributions that cannot be modeled by approximations with more specific rules and provisions, such as the Empirical Rule.
What are the advantages of Norton's theorem?
It is used to reduce the complexitiy of the networkAnswerNorton's Theorem is one of several theorems necessary to solve 'complex' circuits -i.e. circuits that are not series, parallel, or series parallel.
Tell some advantages of superposition theorem in circuit analysis?
It is applied not only for the elements f the network but also for the sourcesssss
