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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.
What does probalility distribution mean?
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?
For the normal distribution does it always require a continuity correction?
Use the continuity correction when using the normal distribution to approximate a binomial distribution to take into account the binomial is a discrete distribution and the normal distribution is continuous.
Is in binomial distribution mean always greater then variance?
yes
Does empirical rules always apply to discrete probability distribution?
the empirical rules of probablility applies to the continuous probability distribution
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.
What the ethical issues in probability distribution?
Probability distribution is when all the possible outcomes of a random variation are gathered together and the probability of each outcome is figured out. There are several ethical issues with this one being that it is not always accurate information that is gathered.
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.
What is the probability distribution that can be describe by just one parameter?
There are probably many probability distributions that have just one parameter. The most important one for statistical analysis is probably the Student t distribution.This probability distribution is fully described by a single parameter which is often called "degrees of freedom". The parameter describes the scale of the distribution, and not the location, since the Student t distribution is always centered at zero (unlike the normal distribution, which has a scale parameter, the variance, and a location parameter, the mean).Another example of a distribution that is described with a single parameter is the exponential distribution. Unlike the Student t distribution, it is a distribution that takes only positive values.
What percentage of observations of a normal distribution is reprented by the mean plus or minus 1.96 standard deviations?
The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95
