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No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
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How the symmetric distribution is always normal?
The statement is false. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.
Is in the normal distribution the total area beneath the curve represent the probability for all possible outcomes for a given event?
Yes. The total area under any probability distribution curve is always the probability of all possible outcomes - which is 1.
Why will a density curve always give a probability of zero?
It will not. For the interval (x, x+dx) it may well give a non-zero probability. With a continuous distribution, the probability of any particular value is always 0. What the probability density function gives is the probability that the variable is NEAR the selected value.
What are the requirements of a probability distribution?
It is always non-negative. The sum (or integral) over all possible outcomes is 1.
Difference between a random variable and a probability distribution is?
A random variable is a variable that can take different values according to a process, at least part of which is random.For a discrete random variable (RV), a probability distribution is a function that assigns, to each value of the RV, the probability that the RV takes that value.The probability of a continuous RV taking any specificvalue is always 0 and the distribution is a density function such that the probability of the RV taking a value between x and y is the area under the distribution function between x and y.
