You need to know the standard deviation or standard error to answer the question.
how do i find the median of a continuous probability distribution
0.0375
The area under a normal distribution is one since, by definition, the sum of any series of probabilities is one and, therefore, the integral (or area under the curve) of any probability distribution from negative infinity to infinity is one. However, if you take an interval of a normal distribution, its area can be anywhere between 0 and 1.
A normal distribution is defined by its mean and standard deviation, which are sufficient to describe the entire curve. Once you know these two parameters, you can use the standard normal table (Z-table) to find probabilities for any normal distribution by standardizing values. This process involves converting any normal variable to a standard score (Z-score), which allows you to utilize the same table for all normal distributions. Therefore, only one normal table is needed for any probability under the normal curve.
P(0 < z < 2.53) refers to the probability that a standard normal random variable (z) falls between 0 and 2.53. To find this probability, you would typically look up the z-scores in a standard normal distribution table or use a calculator. The cumulative probability for z = 2.53 is approximately 0.994, and for z = 0, it is 0.5. Therefore, P(0 < z < 2.53) is approximately 0.994 - 0.5 = 0.494.
A researcher wants to go from a normal distribution to a standard normal distribution because the latter allows him/her to make the correspondence between the area and the probability. Though events in the real world rarely follow a standard normal distribution, z-scores are convenient calculations of area that can be used with any/all normal distributions. Meaning: once a researcher has translated raw data into a standard normal distribution (z-score), he/she can then find its associated probability.
how do i find the median of a continuous probability distribution
0.0375
The area under a normal distribution is one since, by definition, the sum of any series of probabilities is one and, therefore, the integral (or area under the curve) of any probability distribution from negative infinity to infinity is one. However, if you take an interval of a normal distribution, its area can be anywhere between 0 and 1.
A normal distribution is defined by its mean and standard deviation, which are sufficient to describe the entire curve. Once you know these two parameters, you can use the standard normal table (Z-table) to find probabilities for any normal distribution by standardizing values. This process involves converting any normal variable to a standard score (Z-score), which allows you to utilize the same table for all normal distributions. Therefore, only one normal table is needed for any probability under the normal curve.
Provided the distribution is Normal, the z-score is the value such that the probability of observing a smaller value is 0.25. Thus z = -0.67449
In the field of analytical measurement, the z-multiplier is a measure of error. It indicates a statistical probability of error. It is calculated using standard formulas for normal distribution.
P(0 < z < 2.53) refers to the probability that a standard normal random variable (z) falls between 0 and 2.53. To find this probability, you would typically look up the z-scores in a standard normal distribution table or use a calculator. The cumulative probability for z = 2.53 is approximately 0.994, and for z = 0, it is 0.5. Therefore, P(0 < z < 2.53) is approximately 0.994 - 0.5 = 0.494.
Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.
This question can only be answered if the probability distribution functions of X1, X2 and X3 are known. They are not and so the question cannot be answered.
If this is the only information that you have then you must use the Poisson distribution.
There is the normal probability density function (pdf) which is given in the attached link. The normal probability cumulative distribution function (cdf) is used to calculate probabilities, and there is no closed form equation for this. Many statistical programs have the cdf built in. Some references are given at the end of the link to find approximate cdf. The cdf, is usually written F(x) and the pdf f(x). F(x) is the integral of f(x) from minus infinity to x.