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92       3 Estimating Data Parameters


              In  parameter estimation one often needs to use percentiles of  random
           distributions. We have seen that before, concerning the application of percentiles
           of the  normal and the Student’s  t distribution. Later  on  we  will need to apply
           percentiles of the chi-square and  F distributions. Statistical software usually
           provides a large  panoply of  probabilistic functions (density and cumulative
           distribution  functions, quantile functions  and  random number generators  with
           particular distributions). In Commands 3.3  we present some of the possibilities.
           Appendix D also provides tables of the most usual distributions.


           Commands 3.3. SPSS, STATISTICA, MATLAB and R commands for obtaining
           quantiles of distributions.


             SPSS          Compute Variable

             STATISTICA  Statistics; Probability Calculator

             MATLAB        norminv(p,mu,sigma) ; tinv(p,df) ;
                           chi2inv(p,df) ; finv(p,df1,df2)
             R             qnorm(p,mean,sd) ; qt(p,df) ;
                           qchisq(p,df) ; qf(p,df1,df2)

           The  Compute Variable    window of SPSS allows the use of functions to
           compute percentiles of distributions, namely the functions Idf.IGauss  , Idf.T  ,
           Idf.Chisq and  Idf.F for the normal, Student’s  t, chi-square and  F
           distributions, respectively.
              STATISTICA provides a  versatile  Probability Calculator   allowing
           among other things the computation of percentiles of many common distributions.
              The MATLAB and R functions allow the computation of quantiles  of the
           normal, t, chi-square and F distributions, respectively.




           3.3  Estimating a Proportion

           Imagine that one wished to estimate the probability of occurrence, p, of a “success”
           event in a series of n Bernoulli trials. A Bernoulli trial is a dichotomous outcome
           experiment (see B.1.1). Let k be the number of occurrences of the success event.
           Then, the unbiased and consistent point estimate of p is (see Appendix C):

                  k
               ˆ
              p =  .
                  n

              For instance, if there are k = 5 successes in n = 15 trials, the point estimate of p
           (estimation of a proportion) is ˆ =p  . 0  33 . Let us now construct an interval
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