Plots approach to probability model validation

Reference no: EM131001543

The data in the following table (Exercise 12.12) shows samples of size n = 20 drawn from four di?erent populations postulated to be normal, N , lognormal L, gamma G, and inverse gamma I, respectively.

XN 9.3745	XL 7.9128	XG 10.0896	XI 0.084029
8.8632	5.9166	15.7336	0.174586
11.4943	4.5327	15.0422	0.130492
9.5733	33.2631	5.5482	0.115567
9.1542	24.1327	18.0393	0.187260
9.0992	5.4151	17.9543	0.100054
10.2631	16.9556	12.5549	0.101405
9.8737	3.9345	9.6640	0.100835
7.8192	35.0376	14.2975	0.097173
10.4691	25.1182	4.2599	0.141233
9.6981	1.1804	19.1084	0.060470
10.5911	2.3503	7.0735	0.127663
11.6526	15.6894	7.6392	0.074183
10.4502	5.8929	14.1899	0.086606
10.0772	8.0254	13.8996	0.084915
10.2932	16.1482	9.7680	0.242657
11.7755	0.6848	8.5779	0.052291
9.3790	6.6974	7.5486	0.116172
9.9202	3.6909	10.4043	0.084339
10.9067	34.2152	14.8254	0.205748

(i) Validate these postulates using the full data sets. Note that the population parameters have not been speci?ed.

(ii) Using only the top half of each data set, repeat (i). For this particular example, what effect, if any, does sample size have on the probability plots approach to probability model validation?

Reference no: EM131001543

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