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.
P(less than 5) represents the probability of a random variable taking on a value less than 5. To calculate this probability, you typically need to know the distribution of the variable (e.g., normal, binomial, etc.) and its parameters. Once the distribution is identified, you can compute the cumulative probability up to 5 using appropriate statistical methods or tables. Without specific context or data, it’s not possible to provide a numerical answer.
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 calculate the variance of the sample data set 353641566071, first find the mean by adding all the values together and dividing by the number of values. Then, compute the squared differences between each value and the mean, and average those squared differences to obtain the variance. The choices for variance would typically be numerical values reflecting the dispersion of the data around the mean.
To obtain a much better, simpler, and more practical understanding of the data distribution.
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.
P(less than 5) represents the probability of a random variable taking on a value less than 5. To calculate this probability, you typically need to know the distribution of the variable (e.g., normal, binomial, etc.) and its parameters. Once the distribution is identified, you can compute the cumulative probability up to 5 using appropriate statistical methods or tables. Without specific context or data, it’s not possible to provide a numerical answer.
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%