The normal distribution is a continuous probability distribution that describes the distribution of real-valued random variables that are distributed around some mean value.
The Poisson distribution is a discrete probability distribution that describes the distribution of the number of events that occur within repeated fixed time intervals, where the mean frequency is a known value, and each interval is independent of the prior interval(s)/event(s).
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
If the paired differences are normal in a test of mean differences, then the distribution used for testing is the
There is abig difference between them..gamma is a distribution but central limit theorm is just like a method or technique u use to approximate gamma to another distriution which is normal....stupid
A Poisson distribution is appropriate when you have events that occurindependently of one another in space or time,at a constant rate, andoccur singly.The last condition means that the probability of more than one event occurring at any particular point in space or time is zero (or negligible).However, if the parameter (mean) of the Poisson distribution, L, is greater than 10, you may be better off using the Normal approximation, N(L, L), with the appropriate continuity corrections.
The standard normal distribution is a normal distribution with mean 0 and variance 1.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
The uniform distribution is limited to a finite domain, the normal is not.
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
If this is the only information that you have then you must use the Poisson distribution.
standard normal is for a lot of data, a t distribution is more appropriate for smaller samples, extrapolating to a larger set.
I have no idea what you mean by inducing a distribution.If you assume that the number of events - people joining the queue - in a given time interval has a constant average rate and the the events are independent of one another, then arrivals in the queue follow a Poisson distribution.
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
If the paired differences are normal in a test of mean differences, then the distribution used for testing is the
There is abig difference between them..gamma is a distribution but central limit theorm is just like a method or technique u use to approximate gamma to another distriution which is normal....stupid
A Poisson distribution is appropriate when you have events that occurindependently of one another in space or time,at a constant rate, andoccur singly.The last condition means that the probability of more than one event occurring at any particular point in space or time is zero (or negligible).However, if the parameter (mean) of the Poisson distribution, L, is greater than 10, you may be better off using the Normal approximation, N(L, L), with the appropriate continuity corrections.