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Suppose you have n trials of an experiment in which the probability of "success" in each trial is p. Then the probability of r successes is:
nCr*pr*(1-p)n-r for r = 0, 1, ... n.
nCr = n!/[r!*(n-r)!]
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The binomial and Poisson distributions are examples of discrete probability distributions?
discrete & continuous
What are the pros and cons of discrete probability distribution?
Strictly speaking, there are no cons because they are defined for discrete variables only. The only con that I could think of is the difficulty evaluating the moments and other probabilities for some discrete distributions such as the negative binomial.
What are the conditions under which you can expect to be able to use a binomial distribution to model a probability distribution?
Two independent outcomes with constant probabilities.
Does this means that all symmetric distribution are normal Explain?
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.
Will learning about Probability Binomial and Poisson Probability Distributions using Excel make our studies easier?
If you're studying a subject involving or related to statistics and probability, then it will. If you're not, then it won't.
What is the study of mathematical probabilities distributions and deviations?
It is "probability".
The binomial and Poisson distributions are examples of discrete probability distributions?
discrete & continuous
What is normal binomial distribution?
There is no such thing. The Normal (or Gaussian) and Binomial are two distributions.
Are outcomes in binomial distributions dependent on each other?
No.
What kind of distributions are the binomial and poisson distribution?
Discrete
What are the pros and cons of discrete probability distribution?
Strictly speaking, there are no cons because they are defined for discrete variables only. The only con that I could think of is the difficulty evaluating the moments and other probabilities for some discrete distributions such as the negative binomial.
How do you find binomial probabilities using a binomial table?
Read the instructions that accompany the table: they do not all have exactly the same layout.
How do you determine legitimate probability distributions?
Well... the probabilities should add up to exactly 1 and cannot be negative.
What is the shape of the binomial probability distribution in rolling a die?
The distribution depends on what the variable is. If the key outcome is the number on the top of the die, the distribution in multinomial (6-valued), not binomial. If the key outcome is the number of primes, composite or neither, the distribution is trinomial. If the key outcome is the number of sixes, the distribution is binomial with unequal probabilities of success and failure. If the key outcome is odd or even the distribution is binomial with equal probabilities for the two outcomes. Thus, depending on the outcome of interest the distribution may or may not be binomial and, even when it is binomial, it can have different parameters and therefore different shapes.
What are the conditions under which you can expect to be able to use a binomial distribution to model a probability distribution?
Two independent outcomes with constant probabilities.
List of common symmetric distributions?
A small partial list includes: -normal (or Gaussian) distribution -binomial distribution -Cauchy distribution
When is a binomial distribution cumulative?
It is cumulative when you add together the probabilities of all events resulting in the given number or fewer successes.
