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Level I

Format prediction expression to include quadratic terms

Does someone know how to output a prediction expression using quadratic terms.

I wan to output expression looking like: y = A + Bx + Cx2 - Dx3 instead of y = A + Bx + (x-0,5) * [(x-0,5)*C] * ...

1 ACCEPTED SOLUTION

Accepted Solutions
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Staff

Re: Format prediction expression to include quadratic terms

Hi,

If you save the prediction formula to the data table, and then open the formula in the formula editor, select "Simpliy" from the second red-hotspot (see picture below).

5 REPLIES 5
Highlighted
Staff

Re: Format prediction expression to include quadratic terms

Hi,

If you save the prediction formula to the data table, and then open the formula in the formula editor, select "Simpliy" from the second red-hotspot (see picture below).

Highlighted
Level I

Re: Format prediction expression to include quadratic terms

Thanks!  pretty straight forward...

Highlighted
Level VI

Re: Format prediction expression to include quadratic terms

Hi @Inigo,

It sounds like you're in the Fit Y by X platform. If that's the case, you should be able to go to the red hotbutton, select Fit Special, then choose "No Transformation" radio buttons for both Y and X (assuming the data doesn't need to be transformed), then choose the polynomial degree and UNCHECK the box "Centered Polynomial".

The centered polynomial is there by default as it allows for a better estimation of the coefficients of the fit.

Hope this helps!,

DS

Highlighted
Level I

Re: Format prediction expression to include quadratic terms

I got a fomular for mean confidence limit of a fit model. The expression is extreme large as belows,

``````1.96625963553186 * Sqrt(

[0.0287292895751759 -0.00666060427133376 -0.00420705348654747
-0.000101067994317605 -0.0151558320176791 -0.018006675429998
-0.0105828228409277 0.0525931189072001 -0.00307760776478522
-0.00570142227559169 -0.00611284035501395 -0.0209229212015547
-0.0106347675953972 0.0525931189072001 -0.0128016412797167
-0.00356831792174249 -0.0167269977173011 0.00927090256437917
-0.00358411899712196,
-0.00666060427133376 0.0158382917241673 0.00410842885472875
0.00158305896356562 0.00423442416487127 -0.00049477545256354
-0.000817868717192713 0.00263734766595279 -0.00440057005571037
0.00130152304853031 -0.00127260662386009 -0.00011528684291611
0.000314668319013466 0.00263734766595279 -0.00178122861070318
-0.00205459748182266 0.00303920668921454 -0.00906240060991746
0.00914757285926523,
-0.00420705348654747 0.00410842885472875 0.0102214590502578
0.00262807321722145 0.0026654907331227 -0.000953584762348311
-0.000504910048423059 -0.00335203317054354 0.000315784946773894
0.000907927201776815 -0.00131435643870052 -0.000590273074347901
0.00357311675973109 -0.00335203317054354 -0.00114710879851391
-0.000906821092331919 0.00495431898712722 0.000688897706166622
0.00197347468700018,
-0.000101067994317605 0.00158305896356562 0.00262807321722145
0.0164764002633817 0.00246065900285425 -0.00149703170638324
-0.00328183774362992 0.000649182560833079 0.00127619111302658
-0.00468114540689985 0.000997520645385026 -0.0019928296860267
0.00161500892161912 0.000649182560833082 -0.00264578880577995
0.00106718826229541 0.0042120105942121 0.000510838716778689
0.00266534906695824,
-0.0151558320176791 0.00423442416487127 0.0026654907331227
0.00246065900285425 0.117647453338642 0.0100446758767068 0.0023045234028937
-0.0600811539449608 -0.0038796568611528 -0.00317015079634598
-0.00108032223173412 0.0113936332689033 0.00247729560164183
-0.0600811539449608 0.00479301167527537 -0.00356587017480307
0.00834904973644429 -0.000472225416095427 -0.000670698384850352,
-0.018006675429998 -0.00049477545256354 -0.000953584762348311
-0.00149703170638324 0.0100446758767068 0.0377163708141474
0.00953084423684981 -0.0532302927543955 0.00363727880259079
0.00403818130411868 0.00614058033281912 0.0181055932574402
0.00759246318330849 -0.0532302927543955 0.012027509206273
0.00372904066454776 0.011643459524991 0.000395857625121327
-0.00115783914329167,
-0.0105828228409277 -0.000817868717192713 -0.000504910048423059
-0.00328183774362992 0.0023045234028937 0.00953084423684981
0.127449881862321 -0.0610788972957996 -0.00374826495168063
-0.00276267734110575 -0.00145076821573348 0.0108263485342684
0.000239412168812623 -0.0610788972957996 0.00481922695478737
-0.00381085668543456 0.00396825687274993 0.000574343023851935
-0.00131240178043079,
0.0525931189072001 0.00263734766595279 -0.00335203317054354
0.000649182560833079 -0.0600811539449608 -0.0532302927543955
-0.0610788972957996 0.879292222162986 -0.0671585491960545
-0.0663632831540067 -0.0631804285920365 -0.0521664626950418
-0.0635368104539107 -0.120707777837014 -0.0586769037238872
-0.0659606730287553 -0.059162806946147 -0.00306400387811108
0.00302588320632518,
-0.00307760776478522 -0.00440057005571037 0.000315784946773894
0.00127619111302658 -0.0038796568611528 0.00363727880259079
-0.00374826495168063 -0.0671585491960545 0.192814120948293
-0.00945658351920164 -0.00606262133808362 0.0046914027089562
-0.004438884246623 -0.0671585491960545 -0.00115357469371654
-0.00812915005220371 -0.00087088242454232 0.00278677511153939
-0.00127857569703387,
-0.00570142227559169 0.00130152304853031 0.000907927201776815
-0.00468114540689985 -0.00317015079634598 0.00403818130411868
-0.00276267734110575 -0.0663632831540067 -0.00945658351920164
0.19293648386297 -0.00697067224578166 0.00544241984620045
-0.00495902048766524 -0.0663632831540068 -0.000807750615685828
-0.00937786434985094 -0.00134000398654133 -0.00104252061913907
0.00131431381988893,
-0.00611284035501395 -0.00127260662386009 -0.00131435643870052
0.000997520645385026 -0.00108032223173412 0.00614058033281912
-0.00145076821573348 -0.0631804285920365 -0.00606262133808362
-0.00697067224578166 0.162996130993493 0.00719901808495707
-0.00292685144986197 -0.0631804285920366 0.00105827907065204
-0.00605427137511552 0.000999373343633356 0.000186428893916974
0.00063451714890348,
-0.0209229212015547 -0.00011528684291611 -0.000590273074347901
-0.0019928296860267 0.0113936332689033 0.0181055932574402 0.0108263485342684
-0.0521664626950418 0.0046914027089562 0.00544241984620045
0.00719901808495707 0.0234530300552286 0.00888321057426696
-0.0521664626950419 0.0132735888470266 0.00478639995524257
0.0129679503540653 -0.00241482201075779 0.000163736468384719,
-0.0106347675953972 0.000314668319013466 0.00357311675973109
0.00161500892161912 0.00247729560164183 0.00759246318330849
0.000239412168812623 -0.0635368104539107 -0.004438884246623
-0.00495902048766524 -0.00292685144986197 0.00888321057426696
0.143485683962649 -0.0635368104539108 0.00260233610357529
-0.0050905739347665 0.00540184995175053 0.00143688870211674
-0.000606778563122093,
0.0525931189072001 0.00263734766595279 -0.00335203317054354
0.000649182560833082 -0.0600811539449608 -0.0532302927543955
-0.0610788972957996 -0.120707777837014 -0.0671585491960545
-0.0663632831540068 -0.0631804285920366 -0.0521664626950419
-0.0635368104539108 0.879292222162987 -0.0586769037238873
-0.0659606730287554 -0.0591628069461471 -0.00306400387811108
0.00302588320632518,
-0.0128016412797167 -0.00178122861070318 -0.00114710879851391
-0.00264578880577995 0.00479301167527537 0.012027509206273
0.00481922695478737 -0.0586769037238872 -0.00115357469371654
-0.000807750615685828 0.00105827907065204 0.0132735888470266
0.00260233610357529 -0.0586769037238873 0.0982860349413661
-0.00128039865615439 0.0063811027178823 0.0013092810433933
-0.00250636341693975,
-0.00356831792174249 -0.00205459748182266 -0.000906821092331919
0.00106718826229541 -0.00356587017480307 0.00372904066454776
-0.00381085668543456 -0.0659606730287553 -0.00812915005220371
-0.00937786434985094 -0.00605427137511552 0.00478639995524257
-0.0050905739347665 -0.0659606730287554 -0.00128039865615439
0.191641294669898 -0.00125390488412485 0.000836515448322577
0.000156243937419144,
-0.0167269977173011 0.00303920668921454 0.00495431898712722
0.0042120105942121 0.00834904973644429 0.011643459524991 0.00396825687274993
-0.059162806946147 -0.00087088242454232 -0.00134000398654133
0.000999373343633356 0.0129679503540653 0.00540184995175053
-0.0591628069461471 0.0063811027178823 -0.00125390488412485
0.0945703277688909 0.000719840674021227 -0.000226754921770752,
0.00927090256437917 -0.00906240060991746 0.000688897706166622
0.000510838716778689 -0.000472225416095427 0.000395857625121327
0.000574343023851935 -0.00306400387811108 0.00278677511153939
-0.00104252061913907 0.000186428893916974 -0.00241482201075779
0.00143688870211674 -0.00306400387811108 0.0013092810433933
0.000836515448322577 0.000719840674021227 0.0230396533896294
-0.0161438569971946,
-0.00358411899712196 0.00914757285926523 0.00197347468700018
0.00266534906695824 -0.000670698384850352 -0.00115783914329167
-0.00131240178043079 0.00302588320632518 -0.00127857569703387
0.00131431381988893 0.00063451714890348 0.000163736468384719
-0.000606778563122093 0.00302588320632518 -0.00250636341693975
0.000156243937419144 -0.000226754921770752 -0.0161438569971946
0.017559095893398],
[1] || Design Nom(
:Product Family,
{"E", "G", "Y", "Z"}
) || Design Nom(
:Carrier,
{"Am", "AM", "AT", "CT", "deut", "NA", "Or",
"Retail", "SP", "TIM", "TM", "TF", "VZN", "VDF"
}
) || Design Nom( :Name( "I/M" ), {"F", "I", "O"} )
) * 3.49923396646939
)``````

The first part of formular is 19 by 19 matrix, my question is how to deal the second part of vec quadratic([19 by 19 matrix],

[1] || Design Nom(

:Product Family,

{"E", "G", "Y", "Z"}

) || Design Nom(

:Carrier,

{"Am", "AM", "AT", "CT", "deut", "NA", "Or",

"Retail", "SP", "TIM", "TM", "TF", "VZN", "VDF"

}

) || Design Nom( :Name( "I/M" ), {"F", "I", "O"} )

)

the second part has three sections, 4 in Product  Families, 14 in Carrier, 3 in I/M, total is 21 elements, I am trying to port formula to other software language. I need support to understand this formular.

Highlighted
Staff

Re: Format prediction expression to include quadratic terms

Please see the help for this function. The first argument is the covariance matrix S. The second argument, formed by column concatenation, is the model matrix X.

Learn it once, use it forever!
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