DSdsV2_2 Weibull_7:*3G^Signal Noise RatioDExperimentResponsep  6Signal Noise Ratio  ^|8@dd + =q%7#X1Tahoma1Tahoma1Tahoma1Tahoma1Tahoma"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)5*0_("$"* #,##0_);\("$"* #,##0\);_("$"* "-"_);_(@_),)'_(* #,##0_);\(* #,##0\);_(* "-"_);_(@_)=,8_("$"* #,##0.00_);\("$"* #,##0.00\);_("$"* "-"??_);_(@_)4+/_(* #,##0.00_);\(* #,##0.00\);_(* "-"??_);_(@_)                + ) , *  H   @   8@ 8 0   @ 0  x@@ x@ x  @  @   8@ 8 8 0@ 0 0 0 @ 0 0 x@@ x@ x  @     8@ 8   @ 0   @ 0  x@@$x@$x  @$8@ 8 8 0@ 0 0 0 @ 0 0 x@@$x@$x  @$ Sheet1 Sheet2 Sheet3   dMbP?_*+%&A Page &P&?'?(?)?"d??I@}F} F} F}I F}& F@@@@@@@@@ @ @ @@@@@@@@@@@@@@= ANOVA Table>>>>>?5Source of Variation6Degrees of Freedom!6Sum of Squares [Partial]6Mean Squares [Partial]6F Ratio6P Value78Model~9 @9]Fx[3@9 q[@ 9- 9-78 A:Interference~9@9}8g@9}8g @ 9- 9-78 B:Wall Thickness~9@9#@9#? 9- 9-78 C:Insertion Depth~9@9镲L@9L@ 9- 9-78 D:Percent Adhesive~9@9io@9io? 9- 9-78Residual~999997:Total~; @;]Fx[3@;;;< 3S = 4- 3R-sq =~ 2? 3 R-sq(adj) = 4-jaThe following selected effects are aliased with other effects in the model and have been removed:AB, AC, AD, BC, BD, CDIRegression InformationJJJJJK @TermA CoefficientAStandard ErrorALow CIAHigh CIAT ValueBP ValueC InterceptD(\¥3@ D- D- D- D- E- CA[1]D&† D- D- D- D- E- CA[2]D= ףp=? D- D- D- D- E- CB[1]D7[ A D- D- D- D- E- CB[2]DHzG? D- D- D- D- E- CC[1]D6>W[ D- D- D- D- E- CC[2]Dףp= ? D- D- D- D- E- CD[1]DV_? D- D- D- D- E- FD[2]G@a+տ G- G- G- G- H->dd&   dMbP?_*+%&A Page &P&?'?(?)?"d??@}F} F} F}I F}& F@@@@@@@@@ @ @ @@@@@@@@@@@@@@@@@ ANOVA Table!!!!!"Source of VariationDegrees of Freedom!Sum of Squares [Partial]Mean Squares [Partial]F RatioP ValueModel~ @x $(~??  A:Interference~@镲?w#? - - B:Wall Thickness~@_Q[?H}M? - - C:Insertion Depth~@&†?? - - D:Percent Adhesive~@Ǻ?Ǻ? - - Residual~   Total~  @ x $(~? ; 2S = * R-sq = 100.0000% R-sq(adj) = 100.0000%lc* The following selected effects are aliased with other effects in the model and have been removed:AB, AC, AD, BC, BD, CD/Regression Information000001#$$$$$% &Term' Coefficient'Standard Error'Low CI'High CI'T Value(P Value#$$$$$%) Intercept*8gDio? *- *- *- *- +- )A[1]*[ A *- *- *- *- +- )A[2]*{Gz *- *- *- *- +- )B[1]*n *- *- *- *- +- )B[2]*a2U0* *- *- *- *- +- )C[1]*:H? *- *- *- *- +- )C[2]*S㥛İ *- *- *- *- +- )D[1]*$(~k? *- *- *- *- +- ,D[2]-I +v? -- -- -- -- .->dd&   dMbP?_*+%&A Page &P&?'?(?)?"d??I@}F} F} F}I F}& F@@@@@@@@@ @ @ @@@@@@@@@@@@@@@@@ ANOVA Table!!!!!"Source of VariationDegrees of Freedom!Sum of Squares [Partial]Mean Squares [Partial]F RatioP ValueModel~ @ @x?  A:Interference~@㥛 ?r鷯? - - B:Wall Thickness~@{/L ?]Fx ? - - C:Insertion Depth~@sAO@;M O? - - D:Percent Adhesive~@&S?&S? - - Residual~   Total~  @  @ ; 2S = * R-sq = 100.0000% R-sq(adj) = 100.0000%lc* The following selected effects are aliased with other effects in the model and have been removed:AB, AC, AD, BC, BD, CD/Regression Information000001#$$$$$% &Term' Coefficient'Standard Error'Low CI'High CI'T Value(P Value#$$$$$%) Intercept*8gDiop9@ *- *- *- *- +- )A[1]*hs߿ *- *- *- *- +- )A[2]*~8gDi? *- *- *- *- +- )B[1]*Bfjۿ *- *- *- *- +- )B[2]*/$? *- *- *- *- +- )C[1]*7A` *- *- *- *- +- )C[2]*3? *- *- *- *- +- )D[1]*9#J? *- *- *- *- +- ,D[2]-~jt -- -- -- -- .->dd&  =8X1Tahoma1Tahoma1Tahoma1Tahoma1Tahoma"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)5*0_("$"* #,##0_);\("$"* #,##0\);_("$"* "-"_);_(@_),)'_(* #,##0_);\(* #,##0\);_(* "-"_);_(@_)=,8_("$"* #,##0.00_);\("$"* #,##0.00\);_("$"* "-"??_);_(@_)4+/_(* #,##0.00_);\(* #,##0.00\);_(* "-"??_);_(@_)                + ) , *  H Sheet1 Sheet2 Sheet3   dMbP?_*+%&A Page &P&?'?(?)?"d??@}& F>dd&   dMbP?_*+%&A Page &P&?'?(?)?"d??@}& F>dd&   dMbP?_*+%&A Page &P&?'?(?)?"d??@}& F>dd&  ! 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Data Means  &MDCR-T3;0;0; &MDCR-F &MDCR-S-$ &MDCR-T4;0;0; &MDCR-F\"Tahoma ww @w?f- !0.000R$+ &MDCR-T5;0;0; &MDCR-F &MDCR-S$   &MDCR-T4;0;0; &MDCR-F !4.000R$} &MDCR-T5;0;0; &MDCR-F &MDCR-S-{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F-$ &MDCR-T4;0;0; &MDCR-F !0.800R$n &MDCR-T5;0;0; &MDCR-F &MDCR-S-{$  &MDCR-T4;0;0; &MDCR-F{$  &MDCR-T4;0;0; &MDCR-F{$& & &MDCR-T4;0;0; &MDCR-F{$3 3 &MDCR-T4;0;0; &MDCR-F-$AA &MDCR-T4;0;0; &MDCR-F !1.600R$ &MDCR-T5;0;0; &MDCR-F &MDCR-S-{$OO &MDCR-T4;0;0; &MDCR-F{$\\ &MDCR-T4;0;0; &MDCR-F{$jj &MDCR-T4;0;0; &MDCR-F{$ww &MDCR-T4;0;0; &MDCR-F-$ &MDCR-T4;0;0; &MDCR-F !2.400R$ &MDCR-T5;0;0; &MDCR-F &MDCR-S-{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F-$ &MDCR-T4;0;0; &MDCR-F !3.200R$9 &MDCR-T5;0;0; &MDCR-F &MDCR-S-{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F{$ &MDCR-T4;0;0; &MDCR-F-{${$  &MDCR-T4;0;0; &MDCR-F !17.000$ &MDCR-T5;0;0; &MDCR-F &MDCR-S  &MDCR-T4;0;0; &MDCR-F !21.0005 &MDCR-T5;0;0; &MDCR-F &MDCR-S-  &MDCR-T4;0;0; &MDCR-F !17.800N &MDCR-T5;0;0; &MDCR-F &MDCR-S  &MDCR-T4;0;0; &MDCR-F !18.600 &MDCR-T5;0;0; &MDCR-F &MDCR-S))  &MDCR-T4;0;0; &MDCR-F !19.400 &MDCR-T5;0;0; &MDCR-F &MDCR-Sb b  &MDCR-T4;0;0; &MDCR-F !20.200 &MDCR-T5;0;0; &MDCR-F &MDCR-S- %N pw&.MDCR-T&8;5;0;Y Mean(A:Interference Mean Line) &MDCR-F-- > (&EMDCR-T=8;5;1;Y Mean(A:Interference Mean Line) X = 1.000 Y = 18.658@ &MDCR-F"(&EMDCR-T=8;5;2;Y Mean(A:Interference Mean Line) X = 2.000 Y = 20.725@ &MDCR-FE(&EMDCR-T=8;5;3;Y Mean(A:Interference Mean Line) X = 3.000 Y = 19.559@ &MDCR-F- %N } w &0MDCR-T(8;6;0;Y Mean(B:Wall Thickness Mean Line) &MDCR-F-- ~ )&GMDCR-T?8;6;1;Y Mean(B:Wall Thickness Mean Line) X = 1.000 Y = 18.983 &MDCR-F K )&GMDCR-T?8;6;2;Y Mean(B:Wall Thickness Mean Line) X = 2.000 Y = 20.188 &MDCR-F5  E)&GMDCR-T?8;6;3;Y Mean(B:Wall Thickness Mean Line) X = 3.000 Y = 19.772 &MDCR-F- %N W w&1MDCR-T)8;7;0;Y Mean(C:Insertion Depth Mean Line) &MDCR-F--6  )&HMDCR-T@8;7;1;Y Mean(C:Insertion Depth Mean Line) X = 1.000 Y = 18.354 &MDCR-F % )&HMDCR-T@8;7;2;Y Mean(C:Insertion Depth Mean Line) X = 2.000 Y = 20.205 &MDCR-FE)&HMDCR-T@8;7;3;Y Mean(C:Insertion Depth Mean Line) X = 3.000 Y = 20.383 &MDCR-F- %N w=&2MDCR-T*8;8;0;Y Mean(D:Percent Adhesive Mean Line) &MDCR-F--G  *&IMDCR-TA8;8;1;Y Mean(D:Percent Adhesive Mean Line) X = 1.000 Y = 20.472 &MDCR-F*&IMDCR-TA8;8;2;Y Mean(D:Percent Adhesive Mean Line) X = 2.000 Y = 19.317 &MDCR-Fo E*&IMDCR-TA8;8;3;Y Mean(D:Percent Adhesive Mean Line) X = 3.000 Y = 19.154 &MDCR-F ! Main Effect &MDCR-T6;0;0; &MDCR-F &MDCR-S& &MDCR-T6;0;0; &MDCR-F& &MDCR-T6;0;0; &MDCR-F  !Y MeanI &MDCR-T7;5;0; &MDCR-F &MDCR-S !Y' = Y &MDCR-T7;5;0; &MDCR-F &MDCR-S -Ej &MDCR-T7;5;0; &MDCR-F- &MDCR-T7;5;0; &MDCR-F!A:Interference Mean Line &MDCR-T7;5;0; &MDCR-F &MDCR-S--j &MDCR-T7;6;0; &MDCR-F- &MDCR-T7;6;0; &MDCR-F!B:Wall Thickness Mean Line^ &MDCR-T7;6;0; &MDCR-F &MDCR-S--jD &MDCR-T7;7;0; &MDCR-F-vv &MDCR-T7;7;0; &MDCR-F!C:Insertion Depth Mean Line &MDCR-T7;7;0; &MDCR-F &MDCR-S--Zj &MDCR-T7;8;0; &MDCR-F-(( &MDCR-T7;8;0; &MDCR-F!D:Percent Adhesive Mean Line &MDCR-T7;8;0; &MDCR-F &MDCR-S ! User's Name;! &MDCR-T6;0;0; &MDCR-F &MDCR-S !CompanySg" &MDCR-T6;0;0; &MDCR-F &MDCR-S ! 8/14/2008# &MDCR-T6;0;0; &MDCR-F &MDCR-S ! 5:02:09 PM# &MDCR-T6;0;0; &MDCR-F &MDCR-S-{${$& &MDCR-T6;0;0; &MDCR-F &MDCR-}l--"System?f !-B =X1Tahoma1Tahoma1Tahoma1Tahoma1Tahoma"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)5*0_("$"* #,##0_);\("$"* #,##0\);_("$"* "-"_);_(@_),)'_(* #,##0_);\(* #,##0\);_(* "-"_);_(@_)=,8_("$"* #,##0.00_);\("$"* #,##0.00\);_("$"* "-"??_);_(@_)4+/_(* #,##0.00_);\(* #,##0.00\);_(* "-"??_);_(@_)                + ) , *  H x@@ x@ x  @ 8@ 8   @ 0   @ 0      x@@ x@ x  @ 8@ 8 8 0@ 0 0 0 @ 0 0   2008-08-04 14:21:51   dMbP?_*+%&A Page &P&?'?(?)?"d??2Q@}F} F} F}I F}& F@@@     @@@@@@@@@ @!@"@$@%@'@(@)@*@+@,@-@.@/@0@1@2008-08-04 14:21:51 Alpha: 0.1Transform: Y' = Y00'Alias structure for the selected terms:[A[1]] = A[1] + 0.6667 B[1]C[1] + 0.3333 B[1]C[2] + 0.3333 B[2]C[1] + 0.6667 B[2]C[2] + 0.6667 B[1]D[1] + 0.3333 B[1]D[2] + 0.3333 B[2]D[1] + 0.6667 B[2]D[2] + 0.6667 C[1]D[1] + 0.3333 C[1]D[2] + 0.3333 C[2]D[1] + 0.6667 C[2]D[2][A[2]] = A[2] - 0.3333 B[1]C[1] + 0.3333 B[1]C[2] - 0.6667 B[2]C[1] - 0.3333 B[2]C[2] - 0.3333 B[1]D[1] - 0.6667 B[1]D[2] + 0.3333 B[2]D[1] - 0.3333 B[2]D[2] - 0.3333 C[1]D[1] + 0.3333 C[1]D[2] - 0.6667 C[2]D[1] - 0.3333 C[2]D[2][B[1]] = B[1] + 0.6667 A[1]C[1] + 0.3333 A[1]C[2] + 0.3333 A[2]C[1] + 0.6667 A[2]C[2] + 0.3333 A[1]D[1] - 0.3333 A[1]D[2] - 0.3333 A[2]D[1] - 0.6667 A[2]D[2] + 0.3333 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] - 0.6667 C[2]D[2] [B[2]] = B[2] - 0.3333 A[1]C[1] + 0.3333 A[1]C[2] - 0.6667 A[2]C[1] - 0.3333 A[2]C[2] + 0.3333 A[1]D[1] + 0.6667 A[1]D[2] + 0.6667 A[2]D[1] + 0.3333 A[2]D[2] - 0.6667 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] + 0.3333 C[2]D[2] [C[1]] = C[1] + 0.3333 A[1]B[1] - 0.3333 A[1]B[2] - 0.3333 A[2]B[1] - 0.6667 A[2]B[2] + 0.6667 A[1]D[1] + 0.3333 A[1]D[2] + 0.3333 A[2]D[1] + 0.6667 A[2]D[2] + 0.3333 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] - 0.6667 B[2]D[2] [C[2]] = C[2] + 0.3333 A[1]B[1] + 0.6667 A[1]B[2] + 0.6667 A[2]B[1] + 0.3333 A[2]B[2] - 0.3333 A[1]D[1] + 0.3333 A[1]D[2] - 0.6667 A[2]D[1] - 0.3333 A[2]D[2] - 0.6667 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] + 0.3333 B[2]D[2] [D[1]] = D[1] + 0.6667 A[1]B[1] + 0.3333 A[1]B[2] + 0.3333 A[2]B[1] + 0.6667 A[2]B[2] + 0.3333 A[1]C[1] - 0.3333 A[1]C[2] - 0.3333 A[2]C[1] - 0.6667 A[2]C[2] + 0.3333 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] - 0.6667 B[2]C[2] [D[2]] = D[2] - 0.3333 A[1]B[1] + 0.3333 A[1]B[2] - 0.6667 A[2]B[1] - 0.3333 A[2]B[2] + 0.3333 A[1]C[1] + 0.6667 A[1]C[2] + 0.6667 A[2]C[1] + 0.3333 A[2]C[2] - 0.6667 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] + 0.3333 B[2]C[2]+0"Terms selected to be in the model: Main Effects A, B, C, D2-Way InteractionAB, AC, AD, BC, BD, CD10(Independent terms included in the model: Main Effects A, B, C, D ANOVA TableSource of VariationDegrees of Freedom!Sum of Squares [Partial]Mean Squares [Partial]F RatioP ValueModel~ @]Fx[3@ q[@ - - A:Interference~@}8g@}8g @ - - B:Wall Thickness~@#@#? - - C:Insertion Depth~@镲L@L@ - - D:Percent Adhesive~@io@io? - -Residual~Total~ @]Fx[3@  !S = "-!!R-sq =~!#?"! R-sq(adj) =~"#?j$aThe following selected effects are aliased with other effects in the model and have been removed:%AB, AC, AD, BC, BD, CD'$Regression Information'%%%%%& ('Term(( Coefficient((Standard Error((Low CI((High CI((T Value()P Value)* Intercept)+(\¥3@ )+- )+- )+- )+- ),- **A[1]*+&† *+- *+- *+- *+- *,- +*A[2]++= ףp=? ++- ++- ++- ++- +,- ,*B[1],+7[ A ,+- ,+- ,+- ,+- ,,- -*B[2]-+HzG? -+- -+- -+- -+- -,- .*C[1].+6>W[ .+- .+- .+- .+- .,- /*C[2]/+ףp= ? /+- /+- /+- /+- /,- 0*D[1]0+V_? 0+- 0+- 0+- 0+- 0,- 1-D[2]1.@a+տ 1.- 1.- 1.- 1.- 1/->dd'  =X1Tahoma1Tahoma1Tahoma1Tahoma1Tahoma"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)5*0_("$"* #,##0_);\("$"* #,##0\);_("$"* "-"_);_(@_),)'_(* #,##0_);\(* #,##0\);_(* "-"_);_(@_)=,8_("$"* #,##0.00_);\("$"* #,##0.00\);_("$"* "-"??_);_(@_)4+/_(* #,##0.00_);\(* #,##0.00\);_(* "-"??_);_(@_)                + ) , *  H x@@ x@ x  @  @   8@ 8 0   @ 0  8 0@ 0 0 @ 0    2008-08-11 11:47:00   dMbP?_*+%&A Page &P&?'?(?)?"d??5Q@}F} F} F}I F}& F@@@     @@@@@@@@@@ @"@$@%@'@(@)@*@+@,@-@.@/@0@1@2@3@4@2008-08-11 11:47:00 Alpha: 0.1Transform: Y' = ln Y0('Alias structure for the selected terms:[A[1]] = A[1] + 0.6667 B[1]C[1] + 0.3333 B[1]C[2] + 0.3333 B[2]C[1] + 0.6667 B[2]C[2] + 0.6667 B[1]D[1] + 0.3333 B[1]D[2] + 0.3333 B[2]D[1] + 0.6667 B[2]D[2] + 0.6667 C[1]D[1] + 0.3333 C[1]D[2] + 0.3333 C[2]D[1] + 0.6667 C[2]D[2][A[2]] = A[2] - 0.3333 B[1]C[1] + 0.3333 B[1]C[2] - 0.6667 B[2]C[1] - 0.3333 B[2]C[2] - 0.3333 B[1]D[1] - 0.6667 B[1]D[2] + 0.3333 B[2]D[1] - 0.3333 B[2]D[2] - 0.3333 C[1]D[1] + 0.3333 C[1]D[2] - 0.6667 C[2]D[1] - 0.3333 C[2]D[2][B[1]] = B[1] + 0.6667 A[1]C[1] + 0.3333 A[1]C[2] + 0.3333 A[2]C[1] + 0.6667 A[2]C[2] + 0.3333 A[1]D[1] - 0.3333 A[1]D[2] - 0.3333 A[2]D[1] - 0.6667 A[2]D[2] + 0.3333 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] - 0.6667 C[2]D[2] [B[2]] = B[2] - 0.3333 A[1]C[1] + 0.3333 A[1]C[2] - 0.6667 A[2]C[1] - 0.3333 A[2]C[2] + 0.3333 A[1]D[1] + 0.6667 A[1]D[2] + 0.6667 A[2]D[1] + 0.3333 A[2]D[2] - 0.6667 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] + 0.3333 C[2]D[2] [C[1]] = C[1] + 0.3333 A[1]B[1] - 0.3333 A[1]B[2] - 0.3333 A[2]B[1] - 0.6667 A[2]B[2] + 0.6667 A[1]D[1] + 0.3333 A[1]D[2] + 0.3333 A[2]D[1] + 0.6667 A[2]D[2] + 0.3333 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] - 0.6667 B[2]D[2] [C[2]] = C[2] + 0.3333 A[1]B[1] + 0.6667 A[1]B[2] + 0.6667 A[2]B[1] + 0.3333 A[2]B[2] - 0.3333 A[1]D[1] + 0.3333 A[1]D[2] - 0.6667 A[2]D[1] - 0.3333 A[2]D[2] - 0.6667 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] + 0.3333 B[2]D[2] [D[1]] = D[1] + 0.6667 A[1]B[1] + 0.3333 A[1]B[2] + 0.3333 A[2]B[1] + 0.6667 A[2]B[2] + 0.3333 A[1]C[1] - 0.3333 A[1]C[2] - 0.3333 A[2]C[1] - 0.6667 A[2]C[2] + 0.3333 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] - 0.6667 B[2]C[2] [D[2]] = D[2] - 0.3333 A[1]B[1] + 0.3333 A[1]B[2] - 0.6667 A[2]B[1] - 0.3333 A[2]B[2] + 0.3333 A[1]C[1] + 0.6667 A[1]C[2] + 0.6667 A[2]C[1] + 0.3333 A[2]C[2] - 0.6667 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] + 0.3333 B[2]C[2]+("Terms selected to be in the model: Main Effects A, B, C, D2-Way InteractionAB, AC, AD, BC, BD, CD1((Independent terms included in the model: Main Effects A, B, C, D ANOVA TableSource of VariationDegrees of Freedom!Sum of Squares [Partial]Mean Squares [Partial]F RatioP ValueModel~ @x $(~??  A:Interference~@镲?w#? - - B:Wall Thickness~@_Q[?H}M? - - C:Insertion Depth~@&†?? - - D:Percent Adhesive~@Ǻ?Ǻ? - -Residual~ Total~  @  x $(~?  !;"2S = * R-sq = 100.0000% R-sq(adj) = 100.0000%l$c* The following selected effects are aliased with other effects in the model and have been removed:%AB, AC, AD, BC, BD, CD'Regression Information'( )Term) Coefficient)Standard Error)Low CI)High CI)T Value)"P Value*+# Intercept+8gDio? +- +- +- +- +$- ,#A[1],[ A ,- ,- ,- ,- ,$- -#A[2]-{Gz -- -- -- -- -$- .#B[1].n .- .- .- .- .$- /#B[2]/a2U0* /- /- /- /- /$- 0#C[1]0:H? 0- 0- 0- 0- 0$- 1#C[2]1S㥛İ 1- 1- 1- 1- 1$- 2#D[1]2$(~k? 2- 2- 2- 2- 2$- 3%D[2]3 I +v? 3 - 3 - 3 - 3 - 3&-4'''''''>dd'  =X1Tahoma1Tahoma1Tahoma1Tahoma1Tahoma"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)5*0_("$"* #,##0_);\("$"* #,##0\);_("$"* "-"_);_(@_),)'_(* #,##0_);\(* #,##0\);_(* "-"_);_(@_)=,8_("$"* #,##0.00_);\("$"* #,##0.00\);_("$"* "-"??_);_(@_)4+/_(* #,##0.00_);\(* #,##0.00\);_(* "-"??_);_(@_)                + ) , *  H x@@ x@ x  @  @   8@ 8 0   @ 0  8 0@ 0 0 @ 0    2008-08-11 11:47:00   dMbP?_*+%&A Page &P&?'?(?)?"d??5Q@}F} F} F}I F}& F@@@     @@@@@@@@@@ @"@$@%@'@(@)@*@+@,@-@.@/@0@1@2@3@4@2008-08-11 11:47:00 Alpha: 0.1Transform: Y' = Y0('Alias structure for the selected terms:[A[1]] = A[1] + 0.6667 B[1]C[1] + 0.3333 B[1]C[2] + 0.3333 B[2]C[1] + 0.6667 B[2]C[2] + 0.6667 B[1]D[1] + 0.3333 B[1]D[2] + 0.3333 B[2]D[1] + 0.6667 B[2]D[2] + 0.6667 C[1]D[1] + 0.3333 C[1]D[2] + 0.3333 C[2]D[1] + 0.6667 C[2]D[2][A[2]] = A[2] - 0.3333 B[1]C[1] + 0.3333 B[1]C[2] - 0.6667 B[2]C[1] - 0.3333 B[2]C[2] - 0.3333 B[1]D[1] - 0.6667 B[1]D[2] + 0.3333 B[2]D[1] - 0.3333 B[2]D[2] - 0.3333 C[1]D[1] + 0.3333 C[1]D[2] - 0.6667 C[2]D[1] - 0.3333 C[2]D[2][B[1]] = B[1] + 0.6667 A[1]C[1] + 0.3333 A[1]C[2] + 0.3333 A[2]C[1] + 0.6667 A[2]C[2] + 0.3333 A[1]D[1] - 0.3333 A[1]D[2] - 0.3333 A[2]D[1] - 0.6667 A[2]D[2] + 0.3333 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] - 0.6667 C[2]D[2] [B[2]] = B[2] - 0.3333 A[1]C[1] + 0.3333 A[1]C[2] - 0.6667 A[2]C[1] - 0.3333 A[2]C[2] + 0.3333 A[1]D[1] + 0.6667 A[1]D[2] + 0.6667 A[2]D[1] + 0.3333 A[2]D[2] - 0.6667 C[1]D[1] - 0.3333 C[1]D[2] - 0.3333 C[2]D[1] + 0.3333 C[2]D[2] [C[1]] = C[1] + 0.3333 A[1]B[1] - 0.3333 A[1]B[2] - 0.3333 A[2]B[1] - 0.6667 A[2]B[2] + 0.6667 A[1]D[1] + 0.3333 A[1]D[2] + 0.3333 A[2]D[1] + 0.6667 A[2]D[2] + 0.3333 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] - 0.6667 B[2]D[2] [C[2]] = C[2] + 0.3333 A[1]B[1] + 0.6667 A[1]B[2] + 0.6667 A[2]B[1] + 0.3333 A[2]B[2] - 0.3333 A[1]D[1] + 0.3333 A[1]D[2] - 0.6667 A[2]D[1] - 0.3333 A[2]D[2] - 0.6667 B[1]D[1] - 0.3333 B[1]D[2] - 0.3333 B[2]D[1] + 0.3333 B[2]D[2] [D[1]] = D[1] + 0.6667 A[1]B[1] + 0.3333 A[1]B[2] + 0.3333 A[2]B[1] + 0.6667 A[2]B[2] + 0.3333 A[1]C[1] - 0.3333 A[1]C[2] - 0.3333 A[2]C[1] - 0.6667 A[2]C[2] + 0.3333 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] - 0.6667 B[2]C[2] [D[2]] = D[2] - 0.3333 A[1]B[1] + 0.3333 A[1]B[2] - 0.6667 A[2]B[1] - 0.3333 A[2]B[2] + 0.3333 A[1]C[1] + 0.6667 A[1]C[2] + 0.6667 A[2]C[1] + 0.3333 A[2]C[2] - 0.6667 B[1]C[1] - 0.3333 B[1]C[2] - 0.3333 B[2]C[1] + 0.3333 B[2]C[2]+("Terms selected to be in the model: Main Effects A, B, C, D2-Way InteractionAB, AC, AD, BC, BD, CD1((Independent terms included in the model: Main Effects A, B, C, D ANOVA TableSource of VariationDegrees of Freedom!Sum of Squares [Partial]Mean Squares [Partial]F RatioP ValueModel~ @ @x?  A:Interference~@㥛 ?r鷯? - - B:Wall Thickness~@{/L ?]Fx ? - - C:Insertion Depth~@sAO@;M O? - - D:Percent Adhesive~@&S?&S? - -Residual~ Total~  @  @  !;"2S = * R-sq = 100.0000% R-sq(adj) = 100.0000%l$c* The following selected effects are aliased with other effects in the model and have been removed:%AB, AC, AD, BC, BD, CD'Regression Information'( )Term) Coefficient)Standard Error)Low CI)High CI)T Value)"P Value*+# Intercept+8gDiop9@ +- +- +- +- +$- ,#A[1],hs߿ ,- ,- ,- ,- ,$- -#A[2]-~8gDi? -- -- -- -- -$- .#B[1].Bfjۿ .- .- .- .- .$- /#B[2]//$? /- /- /- /- /$- 0#C[1]07A` 0- 0- 0- 0- 0$- 1#C[2]13? 1- 1- 1- 1- 1$- 2#D[1]29#J? 2- 2- 2- 2- 2$- 3%D[2]3 ~jt 3 - 3 - 3 - 3 - 3&-4'''''''>dd' Y MeanDExperimentResponsep  6Y Mean  fffff1@l3@gffff3@4@433336@93@L3@P2@q= ף5@   ?????????????????????????????????   qq?qq?qqqqqq?qq?qqqqqq?qq?qqqqqq?qq?qqqqqq?  fffff1@l3@gffff3@4@433336@93@L3@P2@q= ף5@  ?????????(\¥3@ ?    I        A        B        C        D        AB        AC        AD        BC        BD        CD                  @Model y[3@ @A:Interferencea@ @B:Wall ThicknessQ+@ @C:Insertion DepthL@ @D:Percent Adhesive0*@  Residual    (\¥3@P.5P.5P.5 InterceptP.5P.5P.5   t@ t@P.5P.5A[1]P.5P.5P.5  7 ףp=?7 ףp=@P.5P.5A[2]P.5P.5P.5  O t@O t@P.5P.5B[1]P.5P.5P.5  RzG?RzG?P.5P.5B[2]P.5P.5P.5  K~K~P.5P.5C[1]P.5P.5P.5  ңp= ?ңp= ?P.5P.5C[2]P.5P.5P.5  g,Œ_?g,Œ_?P.5P.5D[1]P.5P.5P.5  2Œ_,տ2Œ_,P.5P.5D[2]P.5P.5P.5                              (\¥3@ t@7 ףp=?O t@RzG?K~ңp= ?g,Œ_?2Œ_,տ   ?????????????????????????????????????????????????     P.5           I        A        B        C        D        AB        AC        AD        BC        BD        CD       ?    1  LowMediumHigh ThinMediumThick ShallowMediumDeep LowMediumHigh      Y StdDExperimentResponsep  6Y Std  k ,}?6f?o?]/?r?"!|? ?AoZ"?%Wk?   ?????????????????????????????????   qq?qq?qqqqqq?qq?qqqqqq?qq?qqqqqq?qq?qqqqqq?  k ,}?6f?o?]/?r?"!|? ?AoZ"?%Wk?  ?????????l3 o? @?    I        A        B        C        D        AB        AC        AD        BC        BD        CD                  @ModelQy ? @A:InterferenceRC? @B:Wall ThicknessY[? @C:Insertion Depth1ӱ? @D:Percent Adhesive 8 ?  Residual    l3 o?P.5P.5P.5 InterceptP.5P.5P.5  +E|+E|ÿP.5P.5A[1]P.5P.5P.5  D`{D`{ĿP.5P.5A[2]P.5P.5P.5  ;;P.5P.5B[1]P.5P.5P.5  @48@48P.5P.5B[2]P.5P.5P.5  #?#?P.5P.5C[1]P.5P.5P.5  >fH\ð>fH\P.5P.5C[2]P.5P.5P.5  'Wk?'Wk?P.5P.5D[1]P.5P.5P.5  VTv?VT?P.5P.5D[2]P.5P.5P.5                              l3 o?+E|D`{;@48#?>fH\ð'Wk?VTv?   ?????????????????????????????????????????????????     P.5           I        A        B        C        D        AB        AC        AD        BC        BD        CD       ? @   1  LowMediumHigh ThinMediumThick ShallowMediumDeep LowMediumHigh      aAa_MainProject Taguchi Robust Design.doc|8C:\DOCUME~1\hguo\LOCALS~1\Temp\Taguchi Robust Design.docࡱ> %` $ bjbj 4n̟̟c3jjj t  0202028h2t2 Bj33:333"7"7"7AAAAAAA$zChEA "766@"7"7A33A|;|;|;"7z23 3A|;"7A|;|;[>  >33 PZ029s>?A0B{>,bFx:bF>bF >"7"7|;"7"7"7"7"7AA;d"7"7"7B"7"7"7"7 L% L%  Example Taguchi robust design is used to find the appropriate control factor levels in the design to make the system less sensitive to variations in uncontrollable noise factors (i.e. to make the system robust). Consider an experiment that seeks to determine a method to assemble an elastomeric connector to a nylon tube while delivering the requisite pull-off performance suitable for an automotive engineering application.* The primary design objective is to maximize the pull-off force, while secondary considerations are to minimize assembly effort and reduce the cost of the connector and assembly. The controllable and noise factors are: Controllable FactorsNameLevel 1Level 2Level 3AInterferenceLowMediumHighBWall thicknessThinMediumThickCInsertion depthShallowMediumDeepDPercent adhesiveLowMediumHigh Noise FactorsNameUnitLevel -Level +ETimehour24120FTemperatureF72150GHumidity25%75% For the inner (control) array, Taguchi OA L9 (3^4) is used. For the outer (noise) array, a two level full factorial design is applied. The measured response is the pull-off force. Design the Experiment The design matrix and the data are given in the Taguchi Robust Design Example Folio. You can also reproduce the design by the following steps. Step 1: Add a new Standard Folio by selecting Add Folio from the Project menu. Step 2: In the first step of the Design Wizard, select Taguchi Robust Design then click Next.  Step 3: In the second step of the Design Wizard, select Taguchi OA Factorial Design then click Next.  Step 4: In the third step of the Design Wizard, select Single Level Factor Design, 3 Level Design and L9(3^4) for the inner array and specify to use 4 factors.  Step 5: Click the Factor Properties button and, in the Factor Properties window, use the settings shown next then click OK.  Step 6: Click Next and, in the fourth step of the Design Wizard, use the following settings for the outer array.  Step 7: Click the Factor Properties button and, in the Factor Properties window, use the settings shown next then click OK.  Step 8: Click Next to view the design summary, then click Finish to create the Standard Folio containing the two level full factorial design. Alternatively, you can skip the design review step by clicking Finish in the fourth step of the Design Wizard. The run order is randomly generated when you create the design. It may be different from the Folio in the example file. You can conduct the experiment according to the run order in the design matrix and record all the response values. Analysis and Results The design and the data are provided in the Taguchi Robust Design Example Folio. You can proceed using this Folio or you can copy the data to the Folio you just created. Make sure you sort both Folios by the Standard Order column before you copy and paste the data: select Standard Order in the Sort By area in the Control Panel to sort the Data Sheet by the Standard Order column. Once the data set has been entered in the Data Sheet, you can analyze it. Step 1: Double-click the Taguchi Robust Design Example Folio to open it. The Design tab will be displayed. Step 2: In the Control Panel, select Larger from the Signal/Noise Ratio drop-down. Step 3: On the Options page of the Control Panel, select to use Individual Terms for the Y Mean response, which is currently selected in the Response drop-down. Select the Y Std response from the Response drop-down and select to use Individual Terms for it. Do the same for the Signal Noise Ratio response. Step 4: Return to the Main page of the Control Panel and click the Calculate icon. Three responsesY Mean, Y Std and Signal Noise Ratioare automatically calculated and are used as the responses in the analysis. The ANOVA table and the Regression Information table for the model are provided for each response on the Analysis tab, which is added to the Folio upon calculation. Note that the logarithmic transformation is automatically applied to the Y Std response. To view the transformation, you can select the Y Std response in the Control Panel and click the Transformation icon.  In robust design, you can use the effect plots to find proper factor settings to meet both mean and standard deviation requirements for the response. Step 5: Click the Plot icon to add the Plot tab to the Folio. Step 6: Select Main Effects from the Plot Type drop-down in the Control Panel. In the Display Settings area of the Control Panel, select Data Means from the drop-down. The main effect plot for the Y Mean (pull-off force) response is shown next.  Step 7: To view the main effect plot for the Y Std response, select Y Std from the Response drop-down in the Control Panel.  Step 8: To view the main effect plot for the Signal Noise Ratio response, select Signal Noise Ratio from the Response drop-down in the Control Panel.  Conclusions Since the goal is to maximize both signal-to-noise ratio and the pull-off force, some trade-off may need to be made in the selecting of factor settings. Examining the main effect plots for Y Mean and Signal Noise Ratio shows that the medium level for A is clearly the best choice for maximizing signal-to-noise ratio (robustness) and the average pull-off force (Y Mean). It also has a relatively low standard deviation value. For the wall thickness, B, the medium and high level are slightly better than the low level for signal-to-noise ratio; however, the medium is preferred to high level in order to maximize the average pull-off force. From the main effect plot, the better settings for factors C and D can also be determined. In the final analysis, the best settings to maximize signal-to-noise ratio are A (medium), B (medium), C (deep) and D (low), based on the experimental results for maximizing pull-off force. * G. Taguchi, The Taguchi Approach to Parameter Design, Quality Progress, Dec. 1987, pp. 19-26.      HYPERLINK "http://www.reliasoft.com/Weibull/weibull7.htm"  Page  PAGE 8 of  NUMPAGES 8 2008 ReliaSoft Corporation. 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