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1 Chapter 95 Introduction Most statisticians have a set of probability tables that they refer to in doing their statistical wor. This procedure provides you with a set of electronic statistical tables that will let you loo up values for various probability distributions. To run this option, select from the Other menu of the Analysis menu. A window will appear that will let you indicate which probability distribution you want to use along with various input parameters. Select the Calculate button to find and display the results. Many of the probability distributions have two selection buttons to the left of them. The first (left) button selects the inverse probability distribution. An inverse probability distribution is in a form so that when you give it a probability, it calculates the associated critical value. The second (right) button selects the regular probability distribution which is formulated so that when you give it a critical value, it calculates the (left tail) probability. Probability Distributions Beta Distribution The beta distribution is usually used because of its relationship to other distributions, such as the t and F distributions. The noncentral beta distribution function is formulated as follows: Γ( A + B) e L Pr( x A, B, L) = I ( A, B, L) = Γ( A) Γ( B)! < A, < B, L, and x = L A+ B t ( t) dt When the noncentrality parameter (NCP), L, is set to zero, the above formula reduces to the standard beta distribution, formulated as Γ( A + B) Pr(, ) x A B = ( ) ( A) ( B) t A t B Γ Γ dt When the inverse distribution is selected, you supply the probability value and the program solves for. When the regular distribution is selected, you supply and the program solves for the cumulative (left-tail) probability. 95-

2 Binomial Distribution The binomial distribution is used to model the counts of a sequence of independent binary trials in which the probability of a success, P, is constant. The total number of trials (sample size) is N. R represents the number of successes in N trials. The probability of exactly R successes is: N Pr( r = R N, P) = ( ) R P P R N R = N! R!( N R)! The probability of from to R successes is given by: R Pr(, ) = ( ) N r R N P r P P r= r N r Bivariate Normal Distribution The bivariate normal distribution is given by the formula h x + rxy y Pr( x < h, y < r) = exp r π ( r ) x and y follow the bivariate normal distribution with correlation coefficient r. dx dy Chi-Square Distribution The Chi-square distribution arises often in statistics when the normally distributed random variables are squared and added together. DF is the degrees of freedom of the estimated standard error. The noncentral Chi-square distribution function is used in power calculations. The noncentral Chi-square distribution is calculated using the formula: P( df ) = df / df Γ L e Pr( x df, L) = ( + )! P df df / t/ t e dt When the noncentrality parameter (NCP), L, is set to zero, the above formula reduces to the (central) Chi-square distribution. When the inverse distribution is selected, you supply the probability value and the program solves for. When the regular distribution is selected, you supply and the program solves for the cumulative (left-tail) probability. = L 95-

3 Correlation Coefficient Distribution The correlation coefficient distribution is formulated as follows: Pr( r R n, ρ) = r <, ρ <, and R < ( ρ ) n 3 ( )! ( ) ( n )/ ( ) ( n )/ n + i r r n ρ π 3 4 Γ i! R i= F Distribution The F distribution is used in the analysis of variance and in other places the distribution of the ratio of two variances is needed. The degrees of freedom of the numerator variance is DF and the degrees of freedom of the denominator variance is DF. The noncentral-f distribution function is used in power calculations. We calculate the noncentral-f distribution using the following relationship between the F and the beta distribution function. F( df) = F( df ) + df df df Pr( f F df, df, L) = I (,, L) When the noncentrality parameter (NCP), L, is set to zero, the above formula reduces to the standard F distribution When the inverse distribution is selected, you supply the probability value and the program solves for F. When the regular distribution is selected, you supply F and the program solves for the cumulative (left-tail) probability. Hotelling s T Distribution Hotelling s T-Squared distribution is used in multivariate analysis. We calculate the distribution using the following relationship between the F and the T distribution function. ( df + ) ( df ) Pr( x T,, df ) = Pr( x F, +, df ) df df is the number of variables and df is the degrees of freedom associated with the covariance matrix. When the inverse distribution is selected, you supply the probability value and the program solves for T. When the regular distribution is selected, you supply T and the program solves for the cumulative (left-tail) probability. i dr 95-3

4 Gamma Distribution The Gamma distribution is formulated as follows: Pr( g G A, B) = G A / B A B Γ( A) x e dx A x Γ( A) = x e dx < A, < B, and G When the inverse distribution is selected, you supply the probability value and the program solves for G. When the regular distribution is selected, you supply G and the program solves for the cumulative (left-tail) probability. Hypergeometric Distribution The hypergeometric distribution is used to model the following situation. Suppose a sample of size R is selected from a population with N items, M of which have a characteristic of interest. What is the probability that of the items in the sample have this characteristic. The probability of exactly successes is: M N M R Pr( x = N, M, R) = = N! R!( N R)! Maximum(, R-N+M) <= <= Minimum(M, R) Negative Binomial Distribution The negative binomial distribution is used to model the counts of a sequence of independent binary trials in which the probability of a success, P, is constant. The total number of trials (sample size) is N. R represents the number of successes in N trials. Unlie the binomial distribution, the sample size, N, is the variable of interest. The question answered by the negative binomial distribution is: how many tosses of a coin (with probability of a head equal to P) is necessary to achieve R heads and tails. The probability of exactly R successes is: + R Pr( x = R, P) = P R R ( P) = N! R!( N R)! 95-4

5 Normal Distribution The normal distribution is formulated as follows: ( x µ ) Pr( x µ, σ ) = exp πσ σ When the mean is and the variance is, we have the standard normal distribution. The regular normal distribution uses the variable. The standard normal distribution uses the variable Z. Any normal distribution may be transformed to the standard normal distribution using the relationship: z x = µ σ dx Poisson Distribution The Poisson distribution is used to model the following situation. Suppose the average number of accidents at a given intersection is 3.5 per year. What is the probability of having accidents during the next half year? The probability of exactly occurrences with a mean occurrence rate of M is: M Pr( x = M ) = e M! Studentized Range Distribution The studentized range distribution is used whenever the distribution of the ratio of a range and an independent estimate of its standard error is needed. This distribution is used quite often in multiple comparison tests run after an analysis of variance. DF is the degrees of freedom of the estimated standard error (often the degrees of freedom of the MSE). K is the number of items (means) in the sample. The distribution function is given by: df / + df / df Pr(, ) = df s dfs r R df exp ( ) P Rs n dx df Γ P(Rs n) is the probability integral of the range. 95-5

6 Student s t Distribution The t distribution is used whenever the distribution of the ratio of a statistic and its standard error is needed. DF is the degrees of freedom of the estimated standard error. The noncentral-t distribution function is used in power calculations. We calculate the noncentral-t distribution using the following relationship between the t and the beta distribution function. df = df + T Pr( t T df, L) = e = L / ( L / ) df I (,,)! When the noncentrality parameter (NCP), L, is set to zero, the above formula reduces to the (central) Student s t distribution When the inverse distribution is selected, you supply the probability value and the program solves for T. When the regular distribution is selected, you supply T and the program solves for the cumulative (left-tail) probability. Weibull Distribution The Weibull distribution is formulated as follows: ( ) Pr( t T λ, γ ) = exp ( λt ) γ When gamma (γ) equal to one, the distribution simplifies to the exponential distribution. When the inverse distribution is selected, you supply the probability value and the program solves for T. When the regular distribution is selected, you supply T and the program solves for the cumulative (left-tail) probability. 95-6

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