Sorry, I´m not sure if this could be usefull and neither where to post it but I think that after do it, may be it could be a help for someone.
I need the values of the Cumulative Standard Normal Distribution so I made it using Excel (it has this as a function) and export it to be usable in FB.
Is the same you could find here:
https://estadisticaorquestainstrumento. ... on-normal/
They are orderer this way:
40 rows and 10 columns where the increment being in the same row but one column to right is 0.01 and being in the same column but one row to down is 0.1
Code: Select all
Dim i As integer
Dim DNSC (1 To 400) As Double
For i=1 to 400
Read DNSC(i)
Next
Data 0.5,0.503989356,0.507978314,0.511966473,0.515953437,0.519938806,0.523922183,0.52790317,0.531881372,0.535856393
Data 0.539827837,0.543795313,0.547758426,0.551716787,0.555670005,0.559617692,0.563559463,0.567494932,0.571423716,0.575345435
Data 0.579259709,0.583166163,0.587064423,0.590954115,0.594834872,0.598706326,0.602568113,0.606419873,0.610261248,0.614091881
Data 0.617911422,0.621719522,0.625515835,0.629300019,0.633071736,0.636830651,0.640576433,0.644308755,0.648027292,0.651731727
Data 0.655421742,0.659097026,0.662757273,0.666402179,0.670031446,0.67364478,0.67724189,0.680822491,0.684386303,0.687933051
Data 0.691462461,0.694974269,0.698468212,0.701944035,0.705401484,0.708840313,0.712260281,0.715661151,0.719042691,0.722404675
Data 0.725746882,0.729069096,0.732371107,0.735652708,0.7389137,0.742153889,0.745373085,0.748571105,0.75174777,0.754902906
Data 0.758036348,0.761147932,0.764237502,0.767304908,0.770350003,0.773372648,0.776372708,0.779350054,0.782304562,0.785236116
Data 0.788144601,0.791029912,0.793891946,0.796730608,0.799545807,0.802337457,0.805105479,0.807849798,0.810570345,0.813267057
Data 0.815939875,0.818588745,0.82121362,0.823814458,0.82639122,0.828943874,0.831472393,0.833976754,0.836456941,0.83891294
Data 0.841344746,0.843752355,0.84613577,0.848494997,0.85083005,0.853140944,0.8554277,0.857690346,0.85992891,0.862143428
Data 0.864333939,0.866500487,0.868643119,0.870761888,0.872856849,0.874928064,0.876975597,0.878999516,0.880999893,0.882976804
Data 0.88493033,0.886860554,0.888767563,0.890651448,0.892512303,0.894350226,0.896165319,0.897957685,0.899727432,0.901474671
Data 0.903199515,0.904902082,0.906582491,0.908240864,0.909877328,0.911492009,0.913085038,0.914656549,0.916206678,0.917735561
Data 0.919243341,0.920730159,0.922196159,0.92364149,0.9250663,0.92647074,0.927854963,0.929219123,0.930563377,0.931887882
Data 0.933192799,0.934478288,0.935744512,0.936991636,0.938219823,0.939429242,0.940620059,0.941792444,0.942946567,0.944082597
Data 0.945200708,0.946301072,0.947383862,0.948449252,0.949497417,0.950528532,0.951542774,0.952540318,0.953521342,0.954486023
Data 0.955434537,0.956367063,0.957283779,0.958184862,0.959070491,0.959940843,0.960796097,0.96163643,0.96246202,0.963273044
Data 0.964069681,0.964852106,0.965620498,0.966375031,0.967115881,0.967843225,0.968557237,0.969258091,0.969945961,0.97062102
Data 0.97128344,0.971933393,0.97257105,0.973196581,0.973810155,0.97441194,0.975002105,0.975580815,0.976148236,0.976704532
Data 0.977249868,0.977784406,0.978308306,0.97882173,0.979324837,0.979817785,0.98030073,0.980773828,0.981237234,0.9816911
Data 0.982135579,0.982570822,0.982996977,0.983414193,0.983822617,0.984222393,0.984613665,0.984996577,0.985371269,0.985737882
Data 0.986096552,0.986447419,0.986790616,0.987126279,0.987454539,0.987775527,0.988089375,0.988396208,0.988696156,0.988989342
Data 0.98927589,0.989555923,0.989829561,0.990096924,0.99035813,0.990613294,0.990862532,0.991105957,0.991343681,0.991575814
Data 0.991802464,0.99202374,0.992239746,0.992450589,0.992656369,0.992857189,0.993053149,0.993244347,0.993430881,0.993612845
Data 0.993790335,0.993963442,0.994132258,0.994296874,0.994457377,0.994613854,0.994766392,0.994915074,0.995059984,0.995201203
Data 0.995338812,0.995472889,0.995603512,0.995730757,0.995854699,0.995975411,0.996092967,0.996207438,0.996318892,0.996427399
Data 0.996533026,0.99663584,0.996735904,0.996833284,0.996928041,0.997020237,0.997109932,0.997197185,0.997282055,0.997364598
Data 0.99744487,0.997522925,0.997598818,0.9976726,0.997744323,0.997814039,0.997881795,0.997947641,0.998011624,0.998073791
Data 0.998134187,0.998192856,0.998249843,0.99830519,0.998358939,0.99841113,0.998461805,0.998511001,0.998558758,0.998605113
Data 0.998650102,0.998693762,0.998736127,0.998777231,0.998817109,0.998855793,0.998893315,0.998929706,0.998964997,0.998999218
Data 0.999032397,0.999064563,0.999095745,0.999125968,0.999155261,0.999183648,0.999211154,0.999237805,0.999263625,0.999288636
Data 0.999312862,0.999336325,0.999359047,0.999381049,0.999402352,0.999422975,0.999442939,0.999462263,0.999480965,0.999499063
Data 0.999516576,0.99953352,0.999549913,0.99956577,0.999581108,0.999595942,0.999610288,0.999624159,0.999637571,0.999650537
Data 0.999663071,0.999675186,0.999686894,0.999698209,0.999709143,0.999719707,0.999729912,0.999739771,0.999749293,0.99975849
Data 0.999767371,0.999775947,0.999784227,0.99979222,0.999799936,0.999807384,0.999814573,0.999821509,0.999828203,0.999834661
Data 0.999840891,0.999846901,0.999852698,0.999858289,0.999863681,0.99986888,0.999873892,0.999878725,0.999883383,0.999887873
Data 0.9998922,0.99989637,0.999900389,0.99990426,0.99990799,0.999911583,0.999915043,0.999918376,0.999921586,0.999924676
Data 0.999927652,0.999930517,0.999933274,0.999935928,0.999938483,0.999940941,0.999943306,0.999945582,0.999947772,0.999949878
Data 0.999951904,0.999953852,0.999955726,0.999957527,0.999959259,0.999960924,0.999962525,0.999964064,0.999965542,0.999966963