Assuming that the requirements of normality are met, a statement such as the parameter M has the value m, with a 95% confidence interval of (m-a, m+b) means that there is a 95% probability that the true value of M lies between m-a and m+b.
the empirical rules of probablility applies to the continuous probability distribution
Exponential DistributionThe exponential distribution is a very commonly used distribution in reliability engineering. Due to its simplicity, it has been widely employed even in cases to which it does not apply. The exponential distribution is used to describe units that have a constant failure rate.
what else do you want it to apply to
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.
The answer depends on the type of data that you have and what you want to look at.If you have a long time series and want to look for long term trends or for seasonality then you will need to apply some form of smoothing first. Many statistical techniques and tests are best suited for data which are Gaussian (Normal). There are transformations which you might wish to apply to the data to make them more Gaussian. For example, if the data are percentages which are near to 0 or 100 then you might wish to apply the arcsine transform. Another common operation is the log transform.Also, there are different kinds of tests for comparing equality of means, equality of variance (or standard deviation), equality of distribution and so on.
The central limit theorem basically states that for any distribution, the distribution of the sample means approaches a normal distribution as the sample size gets larger and larger. This allows us to use the normal distribution as an approximation to binomial, as long as the number of trials times the probability of success is greater than or equal to 5 and if you use the normal distribution as an approximation, you apply the continuity correction factor.
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.
Answering "What are 6 distribution strategies that you can apply for a company?"
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If this is a normal pension distribution, income tax will apply. The rate depends on your other income and filing status. You may be taxed by your state as well. If this is a premature distribution with no exception, you will be assessed a 10% penalty, or $7,700, in addition to regular income taxes.
the empirical rules of probablility applies to the continuous probability distribution
yes
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The same way you apply normal tape, you retard!
Exponential DistributionThe exponential distribution is a very commonly used distribution in reliability engineering. Due to its simplicity, it has been widely employed even in cases to which it does not apply. The exponential distribution is used to describe units that have a constant failure rate.
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