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Is the total area within any continuous probability distribution is equal to 1.00?
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When do you use uniform distribution?
When, over a given range, the probability that a variable in question lies within a particulat interval is equal to the size of that interval as a proportion of the range.
How do calculate the probability of at most?
For a discrete variable, you add together the probabilities of all values of the random variable less than or equal to the specified number. For a continuous variable it the integral of the probability distribution function up to the specified value. Often these values may be calculated or tabulated as cumulative probability distributions.
In the discrete probability distribution what is the sum of all probabilities?
The sum should equal to 1.
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.
What is a uniform probability distribution?
It is a probability distribution where when all of the values of a random variable occur with equal probability. Say X is the random variable, such as what number shows up when we roll a die. There are 6 possible outcomes, each with a 1/6 probability of showing up. If we create a probability distribution where X= 1,2,3,4,5, or 6, we note P(X=k)=1/k where k is any number between 1 and 6 in this case. The graph will be a rectangle.
Is the total area within a continuous probability distribution is equal to 1?
Yes.
When do you use uniform distribution?
When, over a given range, the probability that a variable in question lies within a particulat interval is equal to the size of that interval as a proportion of the range.
How do calculate the probability of at most?
For a discrete variable, you add together the probabilities of all values of the random variable less than or equal to the specified number. For a continuous variable it the integral of the probability distribution function up to the specified value. Often these values may be calculated or tabulated as cumulative probability distributions.
In the discrete probability distribution what is the sum of all probabilities?
The sum should equal to 1.
What does it mean for a probability to be fair?
A probability is fair if there is no bias in any of the possible outcomes. Said another way, all of the possible outcomes in a fair distribution have an equal probability.
What is the probability that a Poisson random variable x is equal to 5...?
It depends on the parameter - the mean of the distribution.
What is a model in which each outcome has an equal probability of occurring?
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
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.
What information do you need to calculate a probability with a normal distribution?
Only the mean, because a normal distribution has a standard deviation equal to the square root of the mean.
What is the difference between probability distribution functions and probability density functions?
Probability density function (PDF) of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a point in the observation space. The PDF is the derivative of the probability distribution (also known as cummulative distriubution function (CDF)) which described the enitre range of values (distrubition) a continuous random variable takes in a domain. The CDF is used to determine the probability a continuous random variable occurs any (measurable) subset of that range. This is performed by integrating the PDF over some range (i.e., taking the area under of CDF curve between two values). NOTE: Over the entire domain the total area under the CDF curve is equal to 1. NOTE: A continuous random variable can take on an infinite number of values. The probability that it will equal a specific value is always zero. eg. Example of CDF of a normal distribution: If test scores are normal distributed with mean 100 and standard deviation 10. The probability a score is between 90 and 110 is: P( 90 < X < 110 ) = P( X < 110 ) - P( X < 90 ) = 0.84 - 0.16 = 0.68. ie. AProximately 68%.
Why the probability of a continuous variable being equal to an exact value is zero?
Pick a number between 0 and 1. Why is it unlikely that you would pick, say, 0.5495872349857293457293759234579823...? Assuming a uniform distribution, the probability that you would happen to get the first decimal correct is 1/10. The probability that you would get the second decimal correct is 1/10. And so on. So the probability that you would get all the decimal places correct is 1/10*1/10*1/10*.... which converges to zero. This same argument can be made for any continuous distribution. Mathematicians have shied away from using the word "impossible" to describe this situation, since we could immagine it possibly happening. The phrase "almost never" is used in conjunction with a probability of zero.
What value of Z from the standard normal distribution table has an area to the left probability equal to 0.0764?
-1.43 (approx)
