MATRIX inverse distribution functions

The following functions give the value in a specified distribution having a cumulative probability equal to prob, the first argument. Subsequent arguments are the parameters of the distribution. Note the period in each function name.

IDF.BETA. IDF.BETA(prob, shape1, shape2). Numeric. Returns the value from the Beta distribution, with the given shape parameters, for which the cumulative probability is prob.

IDF.CAUCHY. IDF.CAUCHY(prob, loc, scale). Numeric. Returns the value from the Cauchy distribution, with the given location and scale parameters, for which the cumulative probability is prob.

IDF.CHISQ. IDF.CHISQ(prob, df). Numeric. Returns the value from the chi-square distribution, with the specified degrees of freedom df, for which the cumulative probability is prob. For example, the chi-square value that is significant at the 0.05 level with 3 degrees of freedom is IDF.CHISQ(0.95,3).

IDF.EXP. IDF.EXP(p, scale). Numeric. Returns the value of an exponentially decaying variable, with rate of decay scale, for which the cumulative probability is p.

IDF.F. IDF.F(prob, df1, df2). Numeric. Returns the value from the F distribution, with the specified degrees of freedom, for which the cumulative probability is prob. For example, the F value that is significant at the 0.05 level with 3 and 100 degrees of freedom is IDF.F(0.95,3,100).

IDF.GAMMA. IDF.GAMMA(prob, shape, scale). Numeric. Returns the value from the Gamma distribution, with the specified shape and scale parameters, for which the cumulative probability is prob.

IDF.HALFNRM. IDF.HALFNRM(prob, mean, stddev). Numeric. Returns the value from the half normal distribution, with the specified mean and standard deviation, for which the cumulative probability is prob.

IDF.IGAUSS. IDF.IGAUSS(prob, loc, scale). Numeric. Returns the value from the inverse Gaussian distribution, with the given location and scale parameters, for which the cumulative probability is prob.

IDF.LAPLACE. IDF.LAPLACE(prob, mean, scale). Numeric. Returns the value from the Laplace distribution, with the specified mean and scale parameters, for which the cumulative probability is prob.

IDF.LNORMAL. IDF.LNORMAL(prob, a, b). Numeric. Returns the value from the log-normal distribution, with specified parameters, for which the cumulative probability is prob.

IDF.LOGISTIC. IDF.LOGISTIC(prob, mean, scale). Numeric. Returns the value from the logistic distribution, with specified mean and scale parameters, for which the cumulative probability is prob.

IDF.NORMAL. IDF.NORMAL(prob, mean, stddev). Numeric. Returns the value from the normal distribution, with specified mean and standard deviation, for which the cumulative probability is prob.

IDF.PARETO. IDF.PARETO(prob, threshold, shape). Numeric. Returns the value from the Pareto distribution, with specified threshold and scale parameters, for which the cumulative probability is prob.

IDF.SMOD. IDF.SMOD(prob, a, b). Numeric. Returns the value from the Studentized maximum modulus, with the specified parameters, for which the cumulative probability is prob.

IDF.SRANGE. IDF.SRANGE(prob, a, b). Numeric. Returns the value from the Studentized range statistic, with the specified parameters, for which the cumulative probability is prob.

IDF.T. IDF.T(prob, df). Numeric. Returns the value from Student's t distribution, with specified degrees of freedom df, for which the cumulative probability is prob.

IDF.UNIFORM. IDF.UNIFORM(prob, min, max). Numeric. Returns the value from the uniform distribution between min and max for which the cumulative probability is prob.

IDF.WEIBULL. IDF.WEIBULL(prob, a, b). Numeric. Returns the value from the Weibull distribution, with specified parameters, for which the cumulative probability is prob.

PROBIT. PROBIT(prob). Numeric. Returns the value in a standard normal distribution having a cumulative probability equal to prob. The argument prob is a probability greater than 0 and less than 1.

Note: PROBIT(prob) is the inverse of CDFNORM(M) and is a special case of IDF.NORMAL(prob, mean, stddev), where mean = 0, stddev = 1.