The binomial distribution.
They are probability distributions!
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
probability density distribution
The total area of any probability distribution is 1
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.
They are probability distributions!
Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.
No. Normal distribution is a continuous probability.
The statement is true that a sampling distribution is a probability distribution for a statistic.
how do i find the median of a continuous probability distribution
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
None. The full name is the Probability Distribution Function (pdf).
Suppose you have two random variables, X and Y and their joint probability distribution function is f(x, y) over some appropriate domain. Then the marginal probability distribution of X, is the integral or sum of f(x, y) calculated over all possible values of Y.
The probability distribution of an experiment is a function that maps the probability of each possible outcome of the experiment to that outcome.
They are the same. The full name is the Probability Distribution Function (pdf).
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.