You find z = (u - m)/s and the look up the Pr(Z > z) in the tables of cumulative probability of the normal distribution. Note that the tables only give the probabilities for Pr(Z <= z) for z >=0. So Pr(Z > z) = 1 - Pr(Z<=z). Also, if z < 0 then you will need to use the symmetry of Z about the value 0.
If a random variable (RV) X is distributed Normally with mean m and standard deviation sthenZ = (X - m)/s is the corresponding Normal variable which is distributed with mean 0 and variance 1. The distribution of X is difficult to compute but that for Z is readily available. It can be used to find the probabilities of the RV lying in different domains and thereby for testing hypotheses.
Follow these steps:Find all the values that the random variable (RV) can take, x.For each x, find the probability that the RV takes than value, p(x).Multiply them: x*p(x).Sum these over all possible values of x.The above sum is the expected value of the RV, X.
To obtain a much better, simpler, and more practical understanding of the data distribution.
The first step in calculating a p-value is to make a hypothesis of the statistical model for your study. You then assume that the hypothesis is true and calculate the probability of observing an outcome at least as extreme as the one that you did observe. This probability is the p-value.
You are asking a question about experimental probability. The problem with that type of question is that the answer is different each time you run the experiment. That's why we call it experimental probability. The outcome will be different each time you run the experiment.This is different than theoretical probability, where you can compute a probability based on some a priori knowledge of the conditions of the experiment. For instance, if you asked me what the probability of throwing a 3 or a 4 on a 12 number die, I could easily compute that as 2 in 12, or 1 in 6, or about 0.1667. Even multiple experiments can be predicted. For instance, if you asked me what was the probability of throwing a 3 or a 4 on a 12 number die three times in a row, I could also easily compute that as (2 in 12)3 or about 0.004630.Alas, experimental and theoretical probability part company and one does not assure the other, unless you run a very large number of tests but, even then, you only do what we call approachthe theoretical results with the experimental outcome.
This is supposed to be Y > u
we compute it by using their differences
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
b is incorrect while c is virtually meaningless.
If a random variable (RV) X is distributed Normally with mean m and standard deviation sthenZ = (X - m)/s is the corresponding Normal variable which is distributed with mean 0 and variance 1. The distribution of X is difficult to compute but that for Z is readily available. It can be used to find the probabilities of the RV lying in different domains and thereby for testing hypotheses.
Variance is variability and diversity of security from average mean and expected value Variance = standard deviation fo security * co relation (r) devided by standanrd deviation of sensex
Price Variance = (Actual Price/Unit - Budgeted Price/Unit) x Actual Quantity of Output = (AP - SP) x AQ
The probability that mathematics will make a male pregnant is zero!
You can compute GDP using industrial origin by creating a solvable equation and substituting in an X variable as well as a Y variable for the unknowns that you would like to solve for.
Compute the actual and budgeted manufacturing overhead rate
For 3 to 1 odds of winning;Probability of winning:0.25, or;Chance of winning:25%
A variable scale uses a set of adjustable accordion-like compasses to compute the distances between points on a map or diagram.