Car Preferences Analysis
Principal Components and Biplots
******************************************************;
* carprin.sas - PRINCIPAL COMPONENTS ANALYSIS OF CAR *;
* PREFERENCES. Adapted from the SAS Sample Library. *;
******************************************************;
Options PS=55 LS=78 PageNo=1 NoDate;
GOptions Reset=ALL TargetDevice=PDF
NoPrompt HText=1 FText=Swiss HTitle=1 FTitle=Swiss;
/*
* PDF code
*
ODS Listing Close;
Filename GSASFile Dummy;
GOptions Device=PDF FText=Helvetica FTitle=Helvetica;
Options TopMargin=1 BottomMargin=1 LeftMargin=1.0 RightMargin=1.0;
ODS PDF File="CarPrin.pdf";
*/
/*
* HTML code
* Remove length specifications in axes statements.
*
ODS Listing Close;
ODS HTML body="CarPrin.html"
headtext="<title>Car Preferences Biplot Analyses</title>"
gpath="CarPrin"
anchor="CarPrin";
GOptions Device=GIF Transparency NoBorder
HText=1 FText=Swiss HTitle=1 FTitle=Swiss;
*/
Title1 'Preference Analysis of Cars';
%Let Ratings=MPG Reliable Accel Braking Handling Ride Visible Comfort Quiet Cargo;
Data Cars;
Length Origin $8;
Input Make $1-10 Model $12-22 @24 (&Ratings) (1.) Origin $35
@37 (Pref1-Pref25) (1.);
If Origin='E' | Origin='J' Then Import='YES';
Else Import='NO';
If Origin='A' Then Origin='AMC ';
Else If Origin='C' Then Origin='CHRYSLER';
Else If Origin='F' Then Origin='FORD ';
Else If Origin='G' Then Origin='GMC ';
Else If Origin='E' Then Origin='EUROPE ';
Else If Origin='J' Then Origin='JAPAN ';
Else Origin='UNKNOWN';
DataLines;
CADILLAC ELDORADO 3234543533 G 0807990491240508971093809
CHEVROLET CHEVETTE 5335425223 G 0051200423451043003515698
CHEVROLET CITATION 4155555525 G 0453305814161643544747795
CHEVROLET MALIBU 3333444544 G 0627400723121345545668658
FORD FAIRMONT 3324345434 F 0224006715021443530648655
FORD MUSTANG 3244323222 F 0507197705021101850657555
FORD PINTO 4134313222 F 0021000303030201500514078
HONDA ACCORD 5554533433 J 9556897609699952998975078
HONDA CIVIC 5545435434 J 8436709507488852567765075
LINCOLN CONTINENTAL 2453353555 F 0708990592230409962091909
PLYMOUTH GRAN FURY 2134353535 C 0706000434101107333458708
PLYMOUTH HORIZON 4345535235 C 0305005635461302444675655
PLYMOUTH VOLARE 2153333424 C 0405003614021602754476555
PONTIAC FIREBIRD 1153551231 G 1007895613201206958265907
VOLKSWAGEN DASHER 5355545435 E 8458696508877795377895000
VOLKSWAGEN RABBIT 5454535424 E 8458509709695795487885000
VOLVO DL 4524555555 E 9989998909999987989919000
;
Title2 "Judges' Preference Scores";
Proc Print Data=Cars;
Var Make Model Origin Import Pref1-Pref25;
Run;
Title2 "Car Attributes";
Proc Print Data=Cars Label;
Var Model MPG Reliable Accel Braking Handling
Ride Visible Comfort Quiet Cargo;
Label Reliable="Rel" Braking="Brake" Handling="Hand"
Visible="Vis" Comfort="Comf";
Footnote1 "Attributes: Reliability Acceleration Braking Handling";
Footnote2 " Ride Visibility Comfort Quiet Cargo-Space";
Run;
FootNote1;
/*
* Use the macros of Michael Friendly to construct
* the biplots.
*/
%Include "biplot.sas";
%Include "equate.sas";
Title2 "Car Makes - Symmetric Biplot (Alpha=1/2)";
%Biplot(Data=Cars,Var=Pref1-Pref25,Id=Make,FacType=SYM);
Title2 "Car Import? - Symmetric Biplot (Alpha=1/2)";
%Biplot(Data=Cars,Var=Pref1-Pref25,Id=Import,FacType=SYM);
Title2 "Car Models - Symmetric Biplot (Alpha=1/2)";
%Biplot(Data=Cars,Var=Pref1-Pref25,Id=Model,FacType=SYM);
/*
* Put the car data and the biplot results together
* for additional analysis. Specifically, we'll compute
* the correlations between car attributes and the
* principal component scores of the cars.
*/
Data Components;
Merge Cars
Biplot(Where=(_TYPE_="OBS"));
Run;
/*
* Compute Correlations.
*/
Title3 "Correlations Between Component Scores and Car Attributes";
Proc Corr Data=Components NoSimple NoProb OutP=Attributes;
Var Dim1 Dim2;
With MPG Reliable Accel Braking Handling Ride Visible Comfort Quiet Cargo;
Run;
/*
* Plot these correlations as supplementary information in the
* previous biplot. The correlations will be inserted through
* the annotate data set.
*/
Data BiAnno2;
Set BiAnno
Attributes(In=In1 Where=(_TYPE_="CORR"));
If In1 Then
Do;
%Let Multiplier=3.0;
XSys="2"; YSys="2"; X=Dim1*&Multiplier; Y=Dim2*&Multiplier; Color="GREEN";
Function="LABEL"; Text=_NAME_; Position="5"; Size=1; Output;
Function="MOVE"; Output;
X=0; Y=0; Function="DRAW"; Output;
End;
Else Output;
Run;
/*
* Now regenerate the biplot, but with the new annotations.
* I used the MPRINT option to generate the code below from
* the original call to the biplot macro.
*/
Title3 "Includes Correlations Of Components With Car Attributes";
Title4 "Correlations Are Multiplied By &Multiplier";
Symbol1 V=NONE C=BLUE I=NONE L=33;
Symbol2 V=NONE C=RED I=VEC L=20;
Proc GPlot Data=BIPLOT;
plot dim2 * dim1 = _type_
/ Annotate=BIANNO2 Frame NoLegend
VAxis=Axis98 HAxis=Axis99 VMinor=1 HMinor=1;
Run;
Quit;
*ODS PDF Close;
*ODS HTML Close;
ODS Listing;
| Preference Analysis of Cars |
| Judges' Preference Scores |
| Obs |
Make |
Model |
Origin |
Import |
Pref1 |
Pref2 |
Pref3 |
Pref4 |
Pref5 |
Pref6 |
Pref7 |
Pref8 |
Pref9 |
Pref10 |
Pref11 |
Pref12 |
Pref13 |
Pref14 |
Pref15 |
Pref16 |
Pref17 |
Pref18 |
Pref19 |
Pref20 |
Pref21 |
Pref22 |
Pref23 |
Pref24 |
Pref25 |
| 1 |
CADILLAC |
ELDORADO |
GMC |
NO |
0 |
8 |
0 |
7 |
9 |
9 |
0 |
4 |
9 |
1 |
2 |
4 |
0 |
5 |
0 |
8 |
9 |
7 |
1 |
0 |
9 |
3 |
8 |
0 |
9 |
| 2 |
CHEVROLET |
CHEVETTE |
GMC |
NO |
0 |
0 |
5 |
1 |
2 |
0 |
0 |
4 |
2 |
3 |
4 |
5 |
1 |
0 |
4 |
3 |
0 |
0 |
3 |
5 |
1 |
5 |
6 |
9 |
8 |
| 3 |
CHEVROLET |
CITATION |
GMC |
NO |
0 |
4 |
5 |
3 |
3 |
0 |
5 |
8 |
1 |
4 |
1 |
6 |
1 |
6 |
4 |
3 |
5 |
4 |
4 |
7 |
4 |
7 |
7 |
9 |
5 |
| 4 |
CHEVROLET |
MALIBU |
GMC |
NO |
0 |
6 |
2 |
7 |
4 |
0 |
0 |
7 |
2 |
3 |
1 |
2 |
1 |
3 |
4 |
5 |
5 |
4 |
5 |
6 |
6 |
8 |
6 |
5 |
8 |
| 5 |
FORD |
FAIRMONT |
FORD |
NO |
0 |
2 |
2 |
4 |
0 |
0 |
6 |
7 |
1 |
5 |
0 |
2 |
1 |
4 |
4 |
3 |
5 |
3 |
0 |
6 |
4 |
8 |
6 |
5 |
5 |
| 6 |
FORD |
MUSTANG |
FORD |
NO |
0 |
5 |
0 |
7 |
1 |
9 |
7 |
7 |
0 |
5 |
0 |
2 |
1 |
1 |
0 |
1 |
8 |
5 |
0 |
6 |
5 |
7 |
5 |
5 |
5 |
| 7 |
FORD |
PINTO |
FORD |
NO |
0 |
0 |
2 |
1 |
0 |
0 |
0 |
3 |
0 |
3 |
0 |
3 |
0 |
2 |
0 |
1 |
5 |
0 |
0 |
5 |
1 |
4 |
0 |
7 |
8 |
| 8 |
HONDA |
ACCORD |
JAPAN |
YES |
9 |
5 |
5 |
6 |
8 |
9 |
7 |
6 |
0 |
9 |
6 |
9 |
9 |
9 |
5 |
2 |
9 |
9 |
8 |
9 |
7 |
5 |
0 |
7 |
8 |
| 9 |
HONDA |
CIVIC |
JAPAN |
YES |
8 |
4 |
3 |
6 |
7 |
0 |
9 |
5 |
0 |
7 |
4 |
8 |
8 |
8 |
5 |
2 |
5 |
6 |
7 |
7 |
6 |
5 |
0 |
7 |
5 |
| 10 |
LINCOLN |
CONTINENTAL |
FORD |
NO |
0 |
7 |
0 |
8 |
9 |
9 |
0 |
5 |
9 |
2 |
2 |
3 |
0 |
4 |
0 |
9 |
9 |
6 |
2 |
0 |
9 |
1 |
9 |
0 |
9 |
| 11 |
PLYMOUTH |
GRAN FURY |
CHRYSLER |
NO |
0 |
7 |
0 |
6 |
0 |
0 |
0 |
4 |
3 |
4 |
1 |
0 |
1 |
1 |
0 |
7 |
3 |
3 |
3 |
4 |
5 |
8 |
7 |
0 |
8 |
| 12 |
PLYMOUTH |
HORIZON |
CHRYSLER |
NO |
0 |
3 |
0 |
5 |
0 |
0 |
5 |
6 |
3 |
5 |
4 |
6 |
1 |
3 |
0 |
2 |
4 |
4 |
4 |
6 |
7 |
5 |
6 |
5 |
5 |
| 13 |
PLYMOUTH |
VOLARE |
CHRYSLER |
NO |
0 |
4 |
0 |
5 |
0 |
0 |
3 |
6 |
1 |
4 |
0 |
2 |
1 |
6 |
0 |
2 |
7 |
5 |
4 |
4 |
7 |
6 |
5 |
5 |
5 |
| 14 |
PONTIAC |
FIREBIRD |
GMC |
NO |
1 |
0 |
0 |
7 |
8 |
9 |
5 |
6 |
1 |
3 |
2 |
0 |
1 |
2 |
0 |
6 |
9 |
5 |
8 |
2 |
6 |
5 |
9 |
0 |
7 |
| 15 |
VOLKSWAGEN |
DASHER |
EUROPE |
YES |
8 |
4 |
5 |
8 |
6 |
9 |
6 |
5 |
0 |
8 |
8 |
7 |
7 |
7 |
9 |
5 |
3 |
7 |
7 |
8 |
9 |
5 |
0 |
0 |
0 |
| 16 |
VOLKSWAGEN |
RABBIT |
EUROPE |
YES |
8 |
4 |
5 |
8 |
5 |
0 |
9 |
7 |
0 |
9 |
6 |
9 |
5 |
7 |
9 |
5 |
4 |
8 |
7 |
8 |
8 |
5 |
0 |
0 |
0 |
| 17 |
VOLVO |
DL |
EUROPE |
YES |
9 |
9 |
8 |
9 |
9 |
9 |
8 |
9 |
0 |
9 |
9 |
9 |
9 |
9 |
8 |
7 |
9 |
8 |
9 |
9 |
1 |
9 |
0 |
0 |
0 |
| Preference Analysis of Cars |
| Car Attributes |
| Obs |
Model |
MPG |
Rel |
Accel |
Brake |
Hand |
Ride |
Vis |
Comf |
Quiet |
Cargo |
| 1 |
ELDORADO |
3 |
2 |
3 |
4 |
5 |
4 |
3 |
5 |
3 |
3 |
| 2 |
CHEVETTE |
5 |
3 |
3 |
5 |
4 |
2 |
5 |
2 |
2 |
3 |
| 3 |
CITATION |
4 |
1 |
5 |
5 |
5 |
5 |
5 |
5 |
2 |
5 |
| 4 |
MALIBU |
3 |
3 |
3 |
3 |
4 |
4 |
4 |
5 |
4 |
4 |
| 5 |
FAIRMONT |
3 |
3 |
2 |
4 |
3 |
4 |
5 |
4 |
3 |
4 |
| 6 |
MUSTANG |
3 |
2 |
4 |
4 |
3 |
2 |
3 |
2 |
2 |
2 |
| 7 |
PINTO |
4 |
1 |
3 |
4 |
3 |
1 |
3 |
2 |
2 |
2 |
| 8 |
ACCORD |
5 |
5 |
5 |
4 |
5 |
3 |
3 |
4 |
3 |
3 |
| 9 |
CIVIC |
5 |
5 |
4 |
5 |
4 |
3 |
5 |
4 |
3 |
4 |
| 10 |
CONTINENTAL |
2 |
4 |
5 |
3 |
3 |
5 |
3 |
5 |
5 |
5 |
| 11 |
GRAN FURY |
2 |
1 |
3 |
4 |
3 |
5 |
3 |
5 |
3 |
5 |
| 12 |
HORIZON |
4 |
3 |
4 |
5 |
5 |
3 |
5 |
2 |
3 |
5 |
| 13 |
VOLARE |
2 |
1 |
5 |
3 |
3 |
3 |
3 |
4 |
2 |
4 |
| 14 |
FIREBIRD |
1 |
1 |
5 |
3 |
5 |
5 |
1 |
2 |
3 |
1 |
| 15 |
DASHER |
5 |
3 |
5 |
5 |
5 |
4 |
5 |
4 |
3 |
5 |
| 16 |
RABBIT |
5 |
4 |
5 |
4 |
5 |
3 |
5 |
4 |
2 |
4 |
| 17 |
DL |
4 |
5 |
2 |
4 |
5 |
5 |
5 |
5 |
5 |
5 |
| Attributes: Reliability Acceleration Braking Handling |
| Ride Visibility Comfort Quiet Cargo-Space |
| Preference Analysis of Cars |
| Car Makes - Symmetric Biplot (Alpha=1/2) |
| Standardization Type: MEAN (VARDEF = N - 1 ) |
| Singular values and variance accounted for |
| Singular Values |
Percent |
Cum % |
Histogram of % |
| 40.9372 |
45.70 |
45.70 |
**************************************** |
| 29.6861 |
24.03 |
69.73 |
********************* |
| 15.0533 |
6.18 |
75.91 |
***** |
| 14.8005 |
5.97 |
81.89 |
***** |
| 12.6845 |
4.39 |
86.27 |
**** |
| 11.5476 |
3.64 |
89.91 |
*** |
| 10.3740 |
2.93 |
92.84 |
*** |
| 9.3794 |
2.40 |
95.24 |
** |
| 7.5872 |
1.57 |
96.81 |
* |
| 6.4207 |
1.12 |
97.94 |
* |
| 5.5715 |
0.85 |
98.78 |
* |
| 4.2083 |
0.48 |
99.27 |
* |
| 3.7332 |
0.38 |
99.65 |
* |
| 2.4011 |
0.16 |
99.80 |
* |
| 2.1026 |
0.12 |
99.92 |
* |
| 1.6691 |
0.08 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| OBS / VARS ratio: |
1.184863 |
Scale: |
1 |
| |
DIM1 |
DIM2 |
| OBS CADILLAC |
-1.0890 |
2.8099 |
| OBS CHEVROLET |
-1.2400 |
-1.6682 |
| OBS CHEVROLET |
-0.3487 |
-1.1789 |
| OBS CHEVROLET |
-0.8458 |
-0.1667 |
| OBS FORD |
-0.8535 |
-1.1648 |
| OBS FORD |
-0.6958 |
0.0810 |
| OBS FORD |
-1.5640 |
-1.6802 |
| OBS HONDA |
2.2930 |
0.2011 |
| OBS HONDA |
1.6175 |
-0.9371 |
| OBS LINCOLN |
-1.1538 |
2.8987 |
| OBS PLYMOUTH |
-1.5383 |
0.1045 |
| OBS PLYMOUTH |
-0.7050 |
-0.7334 |
| OBS PLYMOUTH |
-0.9415 |
-0.5016 |
| OBS PONTIAC |
-0.6369 |
1.5712 |
| OBS VOLKSWAGEN |
2.3160 |
0.3295 |
| OBS VOLKSWAGEN |
2.1976 |
-0.5758 |
| OBS VOLVO |
3.1881 |
0.6108 |
| VAR Pref1 |
2.3876 |
-0.0198 |
| VAR Pref2 |
0.4785 |
1.1259 |
| VAR Pref3 |
1.2194 |
-0.6201 |
| VAR Pref4 |
0.7679 |
1.1883 |
| VAR Pref5 |
1.2301 |
1.8909 |
| VAR Pref6 |
0.9281 |
2.5777 |
| VAR Pref7 |
1.6738 |
-0.5045 |
| VAR Pref8 |
0.4474 |
-0.0706 |
| VAR Pref9 |
-0.8273 |
1.5006 |
| VAR Pref10 |
1.4685 |
-0.4855 |
| VAR Pref11 |
1.5722 |
0.1970 |
| VAR Pref12 |
1.6258 |
-0.3870 |
| VAR Pref13 |
1.9949 |
-0.1488 |
| VAR Pref14 |
1.4989 |
0.2107 |
| VAR Pref15 |
1.7717 |
-0.5971 |
| VAR Pref16 |
0.0467 |
1.4606 |
| VAR Pref17 |
0.2342 |
1.3605 |
| VAR Pref18 |
1.1957 |
1.0137 |
| VAR Pref19 |
1.4881 |
0.1623 |
| VAR Pref20 |
1.2338 |
-1.2318 |
| VAR Pref21 |
0.1949 |
1.1493 |
| VAR Pref22 |
0.2044 |
-0.6558 |
| VAR Pref23 |
-1.6633 |
1.0337 |
| VAR Pref24 |
-0.4416 |
-1.8515 |
| VAR Pref25 |
-1.4031 |
0.4893 |
| Preference Analysis of Cars |
| Car Import? - Symmetric Biplot (Alpha=1/2) |
| Standardization Type: MEAN (VARDEF = N - 1 ) |
| Singular values and variance accounted for |
| Singular Values |
Percent |
Cum % |
Histogram of % |
| 40.9372 |
45.70 |
45.70 |
**************************************** |
| 29.6861 |
24.03 |
69.73 |
********************* |
| 15.0533 |
6.18 |
75.91 |
***** |
| 14.8005 |
5.97 |
81.89 |
***** |
| 12.6845 |
4.39 |
86.27 |
**** |
| 11.5476 |
3.64 |
89.91 |
*** |
| 10.3740 |
2.93 |
92.84 |
*** |
| 9.3794 |
2.40 |
95.24 |
** |
| 7.5872 |
1.57 |
96.81 |
* |
| 6.4207 |
1.12 |
97.94 |
* |
| 5.5715 |
0.85 |
98.78 |
* |
| 4.2083 |
0.48 |
99.27 |
* |
| 3.7332 |
0.38 |
99.65 |
* |
| 2.4011 |
0.16 |
99.80 |
* |
| 2.1026 |
0.12 |
99.92 |
* |
| 1.6691 |
0.08 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| OBS / VARS ratio: |
1.184863 |
Scale: |
1 |
| |
DIM1 |
DIM2 |
| OBS NO |
-1.0890 |
2.8099 |
| OBS NO |
-1.2400 |
-1.6682 |
| OBS NO |
-0.3487 |
-1.1789 |
| OBS NO |
-0.8458 |
-0.1667 |
| OBS NO |
-0.8535 |
-1.1648 |
| OBS NO |
-0.6958 |
0.0810 |
| OBS NO |
-1.5640 |
-1.6802 |
| OBS YES |
2.2930 |
0.2011 |
| OBS YES |
1.6175 |
-0.9371 |
| OBS NO |
-1.1538 |
2.8987 |
| OBS NO |
-1.5383 |
0.1045 |
| OBS NO |
-0.7050 |
-0.7334 |
| OBS NO |
-0.9415 |
-0.5016 |
| OBS NO |
-0.6369 |
1.5712 |
| OBS YES |
2.3160 |
0.3295 |
| OBS YES |
2.1976 |
-0.5758 |
| OBS YES |
3.1881 |
0.6108 |
| VAR Pref1 |
2.3876 |
-0.0198 |
| VAR Pref2 |
0.4785 |
1.1259 |
| VAR Pref3 |
1.2194 |
-0.6201 |
| VAR Pref4 |
0.7679 |
1.1883 |
| VAR Pref5 |
1.2301 |
1.8909 |
| VAR Pref6 |
0.9281 |
2.5777 |
| VAR Pref7 |
1.6738 |
-0.5045 |
| VAR Pref8 |
0.4474 |
-0.0706 |
| VAR Pref9 |
-0.8273 |
1.5006 |
| VAR Pref10 |
1.4685 |
-0.4855 |
| VAR Pref11 |
1.5722 |
0.1970 |
| VAR Pref12 |
1.6258 |
-0.3870 |
| VAR Pref13 |
1.9949 |
-0.1488 |
| VAR Pref14 |
1.4989 |
0.2107 |
| VAR Pref15 |
1.7717 |
-0.5971 |
| VAR Pref16 |
0.0467 |
1.4606 |
| VAR Pref17 |
0.2342 |
1.3605 |
| VAR Pref18 |
1.1957 |
1.0137 |
| VAR Pref19 |
1.4881 |
0.1623 |
| VAR Pref20 |
1.2338 |
-1.2318 |
| VAR Pref21 |
0.1949 |
1.1493 |
| VAR Pref22 |
0.2044 |
-0.6558 |
| VAR Pref23 |
-1.6633 |
1.0337 |
| VAR Pref24 |
-0.4416 |
-1.8515 |
| VAR Pref25 |
-1.4031 |
0.4893 |
| Preference Analysis of Cars |
| Car Models - Symmetric Biplot (Alpha=1/2) |
| Standardization Type: MEAN (VARDEF = N - 1 ) |
| Singular values and variance accounted for |
| Singular Values |
Percent |
Cum % |
Histogram of % |
| 40.9372 |
45.70 |
45.70 |
**************************************** |
| 29.6861 |
24.03 |
69.73 |
********************* |
| 15.0533 |
6.18 |
75.91 |
***** |
| 14.8005 |
5.97 |
81.89 |
***** |
| 12.6845 |
4.39 |
86.27 |
**** |
| 11.5476 |
3.64 |
89.91 |
*** |
| 10.3740 |
2.93 |
92.84 |
*** |
| 9.3794 |
2.40 |
95.24 |
** |
| 7.5872 |
1.57 |
96.81 |
* |
| 6.4207 |
1.12 |
97.94 |
* |
| 5.5715 |
0.85 |
98.78 |
* |
| 4.2083 |
0.48 |
99.27 |
* |
| 3.7332 |
0.38 |
99.65 |
* |
| 2.4011 |
0.16 |
99.80 |
* |
| 2.1026 |
0.12 |
99.92 |
* |
| 1.6691 |
0.08 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| 0.0000 |
0.00 |
100.00 |
* |
| OBS / VARS ratio: |
1.184863 |
Scale: |
1 |
| |
DIM1 |
DIM2 |
| OBS ELDORADO |
-1.0890 |
2.8099 |
| OBS CHEVETTE |
-1.2400 |
-1.6682 |
| OBS CITATION |
-0.3487 |
-1.1789 |
| OBS MALIBU |
-0.8458 |
-0.1667 |
| OBS FAIRMONT |
-0.8535 |
-1.1648 |
| OBS MUSTANG |
-0.6958 |
0.0810 |
| OBS PINTO |
-1.5640 |
-1.6802 |
| OBS ACCORD |
2.2930 |
0.2011 |
| OBS CIVIC |
1.6175 |
-0.9371 |
| OBS CONTINENTAL |
-1.1538 |
2.8987 |
| OBS GRAN FURY |
-1.5383 |
0.1045 |
| OBS HORIZON |
-0.7050 |
-0.7334 |
| OBS VOLARE |
-0.9415 |
-0.5016 |
| OBS FIREBIRD |
-0.6369 |
1.5712 |
| OBS DASHER |
2.3160 |
0.3295 |
| OBS RABBIT |
2.1976 |
-0.5758 |
| OBS DL |
3.1881 |
0.6108 |
| VAR Pref1 |
2.3876 |
-0.0198 |
| VAR Pref2 |
0.4785 |
1.1259 |
| VAR Pref3 |
1.2194 |
-0.6201 |
| VAR Pref4 |
0.7679 |
1.1883 |
| VAR Pref5 |
1.2301 |
1.8909 |
| VAR Pref6 |
0.9281 |
2.5777 |
| VAR Pref7 |
1.6738 |
-0.5045 |
| VAR Pref8 |
0.4474 |
-0.0706 |
| VAR Pref9 |
-0.8273 |
1.5006 |
| VAR Pref10 |
1.4685 |
-0.4855 |
| VAR Pref11 |
1.5722 |
0.1970 |
| VAR Pref12 |
1.6258 |
-0.3870 |
| VAR Pref13 |
1.9949 |
-0.1488 |
| VAR Pref14 |
1.4989 |
0.2107 |
| VAR Pref15 |
1.7717 |
-0.5971 |
| VAR Pref16 |
0.0467 |
1.4606 |
| VAR Pref17 |
0.2342 |
1.3605 |
| VAR Pref18 |
1.1957 |
1.0137 |
| VAR Pref19 |
1.4881 |
0.1623 |
| VAR Pref20 |
1.2338 |
-1.2318 |
| VAR Pref21 |
0.1949 |
1.1493 |
| VAR Pref22 |
0.2044 |
-0.6558 |
| VAR Pref23 |
-1.6633 |
1.0337 |
| VAR Pref24 |
-0.4416 |
-1.8515 |
| VAR Pref25 |
-1.4031 |
0.4893 |
| Preference Analysis of Cars |
| Car Models - Symmetric Biplot (Alpha=1/2) |
| Correlations Between Component Scores and Car Attributes |
| 10 With Variables: |
MPG Reliable Accel Braking Handling Ride Visible Comfort Quiet Cargo |
| 2 Variables: |
DIM1 DIM2 |
| Pearson Correlation Coefficients, N = 17 |
| |
DIM1 |
DIM2 |
| MPG |
0.60558 |
-0.49903 |
| Reliable |
0.71493 |
0.07910 |
| Accel |
0.18750 |
0.18383 |
| Braking |
0.27273 |
-0.49041 |
| Handling |
0.58817 |
0.16310 |
| Ride |
0.11496 |
0.55193 |
| Visible |
0.40948 |
-0.52502 |
| Comfort |
0.24652 |
0.36435 |
| Quiet |
0.23513 |
0.58765 |
| Cargo |
0.25678 |
-0.03032 |
