Format prediction expression to include quadratic terms

[#1]

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] * ...

[#2 · Accepted solution]

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).

[#3]

Thanks!  pretty straight forward...

[#4]

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

[#5]

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

1.96625963553186 * Sqrt(

	Vec Quadratic(

		[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.  

[#6]

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.