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Does empirical rules always apply to discrete probability distribution?
Wiki User
∙ 11y ago
the empirical rules of probablility applies to the continuous probability distribution
Wiki User
∙ 11y ago
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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.
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.
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
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.
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.
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.
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?
Is binomial probability distribution always negatively 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.No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
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.
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.
The difference between the theoretical and empirical probability?
The term empirical means "based on observation or experiment." An empirical probability is generally, but not always, given with a number indicating the possible percent error (e.g. 80+/-3%). A theoretical probability, however, is one that is calculatedbased on theory, i.e., without running any experiments.Since there is no theory that will calculate the probability that an area will experience an earthquake within a given time frame, the 90% figure is an empirical probability, presumably based on data of major earthquakes in the San Francisco area over past years.
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.
Why is the probability of an event always a number between 0 and 1?
The probability of an event is defined as the ratio of favourable outcomes to total outcomes. In the case of discrete distributions these will be represented by numbers, while for continuous distribution they will be measured as areas. In either case, the first measure is non-negative and the second is positive and so the probability is greater than 0. Also, the number of favourable outcomes cannot be greater than the total so the probability must be at most 1.
What are the requirements of a probability distribution?
It is always non-negative. The sum (or integral) over all possible outcomes is 1.
