A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
In general you cannot. You will need to know more about the distribution of the variable - you cannot assume that the distribution is uniform or Normal.
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?
yes it is
the variance of the uniform distribution is (a+b)/12
No. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
The uniform distribution is limited to a finite domain, the normal is not.
The statement is false. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.
the normal distribution is a bell shape and expeonential is rectangular
They are both continuous, symmetric distribution functions.
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
In general you cannot. You will need to know more about the distribution of the variable - you cannot assume that the distribution is uniform or Normal.
Yes, the uniform probability distribution is symmetric about the mode. Draw the sketch of the uniform probability distribution. If we say that the distribution is uniform, then we obtain the same constant for the continuous variable. * * * * * The uniform probability distribution is one in which the probability is the same throughout its domain, as stated above. By definition, then, there can be no value (or sub-domain) for which the probability is greater than elsewhere. In other words, a uniform probability distribution has no mode. The mode does not exist. The distribution cannot, therefore, be symmetric about something that does not exist.
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?
Uniform distribution
The standard normal distribution is a normal distribution with mean 0 and variance 1.