Compute the probability density function (PDF) for the beta distribution, given the point at which to evaluate the function and the values of the shape parameters. The beta distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that are based on beta distribution probabilities.
Compute the probability density function (PDF) for the chi-square distribution, given the degrees of freedom and the point at which to evaluate the function x. The chi-square distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that consider chi-square distribution probabilities.
Compute the probability density function (PDF) for the continuous uniform distribution, given the point at which to evaluate the function and the upper and lower limits of the distribution. The continuous uniform distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that rely on continuous uniform probabilities.
Compute the probability density function (PDF) for the F-distribution, given the point at which to evaluate the function x, and the numerator and denominator degrees of freedom. The F-distribution PDF is very useful for identifying critical values and assessing probabilities in analytics studies that rely on F-tests.
Compute the probability density function (PDF) for the noncentral t-distribution, given the degrees of freedom, the value of the noncentrality parameter, and a t-value. The noncentral t-distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that incorporate noncentral t-distribution probabilities.
Compute the probability density function (PDF) for the normal distribution, given the point at which to evaluate the function x, the mean, and the standard deviation. The normal distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that are concerned with normal distribution probabilities.
Compute the probability density function (PDF) for the standard normal distribution, given the point at which to evaluate the function x. The standard normal distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that involve standard normal distribution probabilities.
Compute the probability density function (PDF) for the t-distribution, given a t-value and the degrees of freedom. The t-distribution PDF is very useful for identifying critical values and assessing probabilities in analytics studies that rely on t-tests.