SPSS® 12.0 Command Syntax Reference

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676 GLM: Multivariate • Dependent variables not appearing in the MMATRIX subcommand will have a row of zeros in the M matrix. • A number can be specified as a fraction with a positive denominator—for example, 1/3 or –1/3, but 1/–3 is invalid. • The number of columns must be greater than 0. You can specify as many columns as you need. • If more than one MMATRIX subcommand is specified, only the last one is in effect. Example GLM Y1 Y2 Y3 BY A B /MMATRIX = “Y1–Y2” Y1 1 Y2 –1; “Y1–Y3” Y1 1 Y3 –1 “Y2–Y3” Y2 1 Y3 –1 /DESIGN. • In the above example, Y1, Y2, and Y3 are the dependent variables. • The MMATRIX subcommand requests all pairwise comparisons among the dependent variables. • Since LMATRIX was not specified, the L matrix is defined by the estimable function for the intercept effect.
GLM: Repeated Measures GLM is available in the Advanced Models option. GLM dependent varlist [BY factor list [WITH covariate list]] /WSFACTOR=name levels [{DEVIATION [(refcat)] }] name... {SIMPLE [(refcat)] } {DIFFERENCE } {HELMERT } {REPEATED } {POLYNOMIAL [({1,2,3...})]**} { {metric } } {SPECIAL (matrix) } [/MEASURE=newname newname...] [/WSDESIGN=effect effect...] [/REGWGT=varname] [/METHOD=SSTYPE({1 })] {2 } {3**} {4 } [/INTERCEPT=[INCLUDE**] [EXCLUDE]] [/MISSING=[INCLUDE] [EXCLUDE**]] [/PRINT = [DESCRIPTIVE] [HOMOGENEITY] [PARAMETER][ETASQ] [RSSCP] [GEF] [LOF] [OPOWER] [TEST [([SSCP] [LMATRIX] [MMATRIX])]] [/SAVE=[tempvar [(list of names)]] [tempvar [(list of names)]]...] [DESIGN] [/EMMEANS=TABLES({OVERALL })] [COMPARE ADJ(LSD(none)(BONFERRONI)(SIDAK))] {factor } {factor*factor... } {wsfactor } {wsfactor*wsfactor... } {factor*...wsfactor*...] {factor*factor... } [/DESIGN={[INTERCEPT...] }]* {[effect effect...]} * The DESIGN subcommand has the same syntax as is described in GLM: Univariate. ** Default if subcommand or keyword is omitted. Example GLM Y1 TO Y4 BY GROUP /WSFACTOR=YEAR 4. 677
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SPSS® 12.0 Command Syntax Referenc
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Universals This part of the SPSS Sy
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Commands 5 before processing. All u
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Commands 7 • Subcommands begin wi
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Commands 9 • Transformation comma
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Files SPSS reads, creates, and writ
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SPSS-Format Data File Files 13 An S
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SPSS Matrix Data Files Files 15 An
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Files 17 4. VARNAME_ variable (or F
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Figure 6 Dictionary of a matrix sys
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Variables To prepare data for proce
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Keyword TO Variables 23 You can est
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Variable Formats Variables 25 SPSS
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Format type Description Ew.d Scient
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Table 4 Numeric formats with punctu
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Variables 31 PIBHEXw (hexadecimal o
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Variables 33 RBw (real binary): The
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Variables 35 is read as two separat
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Transformation Expressions Transfor
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SQRT(arg) Square root. SQRT(SIBS) i
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The following are suffixes for cont
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Transformation Expressions 43 0≤p
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Domain Errors Transformation Expres
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Transformation Expressions 47 origi
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Transformation Expressions 49 • L
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NOT Logical Operator Transformation
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and COMPUTE lagvar=LAG(var1). EXECU
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Date and Time SPSS reads and writes
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Input Data Specification Date and T
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The LIST output from these commands
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Date and Time 61 • A time interva
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Aggregation Functions Date and Time
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Conversion Functions Date and Time
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Date and Time 67 XDATE.HOUR(arg) Re
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Precautions with Date and Time Vari
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72 ACF and DIFF subcommands. With s
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74 ACF SDIFF Subcommand If the seri
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76 ACF SERROR Subcommand SERROR spe
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ADD DOCUMENT ADD DOCUMENT ’text
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ADD FILES ADD FILES FILE={file} {*
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82 ADD FILES • The resulting file
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84 ADD FILES • Input data files a
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86 ADD FILES FIRST and LAST Subcomm
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88 ADD VALUE LABELS • The added v
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AGGREGATE AGGREGATE OUTFILE={file}
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92 AGGREGATE Example Example • If
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94 AGGREGATE Example AGGREGATE OUTF
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96 AGGREGATE FIN(varlist,value1,val
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98 AGGREGATE • User-missing value
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ALSCAL ALSCAL VARIABLES=varlist [/F
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102 ALSCAL Operations • ALSCAL ca
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104 ALSCAL ASYMMETRIC Asymmetric da
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106 ALSCAL • INITIAL is the defau
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108 ALSCAL differences model propos
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110 ALSCAL • Algorithmic options
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112 ALSCAL Table 2 Types of configu
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Matrix mode Object by object 114 AL
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Matrix mode Object by object Object
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118 Syntax Reference Basic Specific
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120 Syntax Reference Example DATA L
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122 Syntax Reference PLOT Subcomman
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124 Syntax Reference VARNAME_ Strin
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ANOVA ANOVA [VARIABLES=] varlist BY
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Example Example ANOVA 129 ANOVA VAR
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ANOVA 131 Some restrictions apply t
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Table 2 Combinations of COVARIATES
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Multiple Classification Analysis AN
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APPLY DICTIONARY 137 • The applie
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APPLY DICTIONARY 139 target file as
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APPLY DICTIONARY 141 MRSETS = MERGE
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APPLY DICTIONARY 143
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Options Basic Specification Subcomm
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• Only one method can be specifie
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APPLY Subcommand AREG 149 • Itera
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AREG 151
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Options Basic Specification ARIMA 1
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Limitations Example ARIMA 155 • F
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D Order of differencing. Q Moving-a
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Termination Criteria Subcommands CI
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References ARIMA 161 Akaike, H. 197
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Subcommand Order • VARIABLES must
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Example AUTORECODE 165 DATA LIST /
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BEGIN DATA—END DATA BEGIN DATA da
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BREAK BREAK Overview BREAK controls
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CASEPLOT Overview CASEPLOT [VARIABL
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Subcommand Order • Subcommands ca
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LN and NOLOG Subcommands CASEPLOT 1
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CASEPLOT 177 erence line. Figure 3
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CASEPLOT 179 • If FORMAT=REFERENC
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Options CASESTOVARS 181 Automatic c
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The commands: CASESTOVARS 183 SPLIT
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create a new file with the followin
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SEPARATOR Subcommand CASESTOVARS 18
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CATPCA CATPCA is available in the C
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Optional output. You can request op
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CATPCA 193 • The NORMALIZATION su
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SPORD and SPNOM Keywords The follow
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ACTIVE Keyword The ACTIVE keyword h
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MAXITER Subcommand CATPCA 199 MAXIT
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CATPCA 201 The keyword TO in a vari
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TRIPLOT(varlist(varlist))(n) CATPCA
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CATPCA 205 procedures with the succ
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CATREG Overview CATREG is available
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Operations Limitations CATREG 209
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ANALYSIS Subcommand LEVEL Keyword C
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GROUPING Keyword DISTR Keyword MISS
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PRINT Subcommand CATREG 215 The PRI
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SAVE Subcommand OUTFILE Subcommand
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CCF Overview Options CCF [VARIABLES
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CCF 221 • LN transforms the data
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CCF 223 • If a natural log transf
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CLEAR TRANSFORMATIONS Overview CLEA
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Example Overview Options CLUSTER V1
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CLUSTER 229 • The variable list i
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Measures for Binary Data CLUSTER 23
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DICE[(p[,np])] Dice (or Czekanowski
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Y( x, y) ad - bc = ----------------
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CLUSTER 237 MEDIAN Median clusterin
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CLUSTER 239 • This example displa
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Matrix Input CLUSTER 241 • The ma
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Example Example Example DATA LIST F
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COMMENT {COMMENT} text { * } Overvi
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Cumulative distribution functions (
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Cumulative distribution functions (
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Logical functions: RANGE(varname,ra
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String Transformations COMPUTE 253
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Examples COMPUTE 255 • The string
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COMPUTE 257 • Alternatively, you
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Equivalence COMPUTE 259 STRING DEPT
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CONJOINT Overview CONJOINT is avail
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CONJOINT 263 the experimental profi
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CONJOINT 265 • The ADD FILES comm
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CONJOINT 267 subject is asked to as
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CONJOINT 269 • New value labels a
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PLOT Subcommand CONJOINT 271 The PL
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Basic Specification CORRELATIONS 27
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MISSING Subcommand CORRELATIONS 275
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Example CORRELATIONS 277 • CORREL
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CORRESPONDENCE CORRESPONDENCE is av
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Syntax Rules Operations Limitations
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DIMENSION Subcommand CORRESPONDENCE
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CORRESPONDENCE 285 EUCLID Euclidean
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PLOT Subcommand NONE No output othe
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CORRESPONDENCE 289 DIM1...DIMn Nume
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COUNT COUNT varname=varlist(value l
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COXREG COXREG is available in the A
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Basic Specification COXREG 295 •
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VARIABLES Subcommand COXREG 297 VAR
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COXREG 299 • If the categorical v
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COXREG 301 BSTEP Backward stepwise.
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CRITERIA Subcommand COXREG 303 CRIT
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COXREG 305 TABLE Write the survival
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CREATE CREATE new series={CSUM (ser
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Example CREATE NEWVAR1 = DIFF(OLDVA
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LAG Function CREATE 311 LAG creates
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Example CREATE TICKMA = MA(TICKETS,
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T4253H Function CREATE 315 T4253H p
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Overview Options CROSSTABS 317 CROS
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Example CROSSTABS 319 • The varia
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CROSSTABS 321 • VARIABLES names t
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CROSSTABS 323 CORR Display Pearson
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FORMAT Subcommand CROSSTABS 325 By
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CROSSTABS 327 • The split-file gr
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CROSSTABS 329
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Operations CSDESCRIPTIVES 331 • C
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CSDESCRIPTIVES 333 • Plan file an
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CSDESCRIPTIVES 335 • The set of s
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CSDESCRIPTIVES 337
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CSSELECT 339 inclusion probabilitie
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PLAN Subcommand CSSELECT 341 The PL
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Example CSPLAN /PLAN OUTFILE=’c:\
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CSSELECT 345
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Design Block: Stages 2 and 3 CSPLAN
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Example Overview Options CSPLAN SAM
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CSPLAN 351 • A PLAN subcommand is
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Stratified sample design CSPLAN SAM
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CSPLAN 355 • The variable samplew
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CSPLAN 357 Typically the previous w
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CSPL AN 359 • CLUSTER is required
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SIZE Subcommand CSPLAN 361 The SIZE
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MINSIZE Keyword CSPLAN 363 MINSIZE
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Keyword Default Root Name Descripti
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CSPLAN 367 strata only the last one
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CSPLAN 369
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CSTABULATE 371 • This specificati
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CSTABULATE 373 on the left defines
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CSTABULATE 375 • For example, /SU
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ADJUST= {BONFERRONI} ORIGIN=COLUMN
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Example Think of self as liberal or
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Example CTABLES /TABLE HAPPY. Gener
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Scale Variables CTABLES 383 Scale v
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CTABLES 385 • Each summary functi
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Table 2 Summary functions: scale va
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Table 3 Summary functions: multiple
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Missing Values in Summaries CTABLES
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Example CTABLES 393 CTABLES /TABLE
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CTABLES 395 • The SUBTOTAL keywor
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Totals Example CTABLES /TABLE AGE [
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CTABLES 399 CORNER Corner text. Cor
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Pairwise Comparisons of Proportions
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CTABLES 403 EMPTY Fill characters u
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CURVEFIT CURVEFIT [VARIABLES=] varn
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Operations CURVEFIT 407 • When CU
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CURVEFIT 409 • This command fits
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APPLY Subcommand CURVEFIT 411 APPLY
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Example Overview Options Week and y
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Fixed-Format Data DATA LIST 415 •
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DATA LIST 417 LIST Freefield data w
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DATA LIST 419 • RECORDS can be us
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Example DATA LIST 421 INPUT PROGRAM
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Variable Names DATA LIST 423 • Va
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Example * Mixing column-style and F
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DATA LIST 427 • For freefield dat
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DATA LIST 429 • The width (w) por
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DATE DATE keyword [starting value [
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DATE 433 • The starting value is
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DATE 435 • For DAY_, the starting
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Example 5 DATE Y 1950 Q 2 M. DATE 4
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The following is a partial listing
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Example DEFINE sesvars () age sex e
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Example Example * Macro without arg
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DEFINE—!ENDDEFINE 445 • The mac
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DEFINE—!ENDDEFINE 447 • This ex
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Example * Keyword !CHAREND. DEFINE
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DEFINE—!ENDDEFINE 451 !DEFAULT De
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DEFINE—!ENDDEFINE 453 !HEAD (str)
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Conditional Processing DEFINE—!EN
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DEFINE—!ENDDEFINE 457 • The fir
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DEFINE—!ENDDEFINE 459
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DESCRIPTIVES Overview Options DESCR
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Z Scores DESCRIPTIVES 463 The z-sco
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MAX Maximum observed value. SUM Sum
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DISCRIMINANT DISCRIMINANT GROUPS=va
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Basic Specification DISCRIMINANT 46
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DISCRIMINANT 471 • Variables shou
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Example DISCRIMINANT 473 DISCRIMINA
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DISCRIMINANT 475 • You can set FI
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SAVE Subcommand DISCRIMINANT 477 SA
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STATISTICS Subcommand DISCRIMINANT
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• The default keywords for CLASSI
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Matrix Input DISCRIMINANT 483 recor
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Example Example Example * Use matri
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DISPLAY DISPLAY [SORTED] [{NAMES**
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DISPLAY 489 • If the keyword SORT
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Example Example DOCUMENT 491 • If
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Overview DO IF 493 The DO IF—END
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Flow of Control DO IF 495 • If th
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Example DO IF 497 DO IF (YRHIRED GT
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Nested DO IF Structures DO IF 499 T
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DO REPEAT—END REPEAT DO REPEAT st
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Example DO REPEAT—END REPEAT 503
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Figure 2 Output from the PRINT subc
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ECHO ECHO "text". Example Overview
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Operations Example END CASE 509 •
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END CASE 511 • Data are extracted
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END CASE 513 BEGIN DATA 1 AC 91 2 C
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END CASE 515 • The frequencies ta
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Example Example *Select cases. INPU
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EXAMINE EXAMINE VARIABLES=varlist [
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Example EXAMINE VARIABLES=MIPERGAL
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• The method keywords follow the
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STATISTICS Subcommand EXAMINE 525 S
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EXAMINE 527 PAIRWISE Delete cases w
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EXPORT EXPORT OUTFILE=file [/TYPE={
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EXPORT 531 • Tape density—200,
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DROP and KEEP Subcommands EXPORT 53
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EXSMOOTH EXSMOOTH is available in t
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Subcommand Order Syntax Rules Opera
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Table 1 Models for different types
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Example EXSMOOTH 541 EXSMOOTH VAR2
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INITIAL Subcommand EXSMOOTH 543 •
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References EXSMOOTH 545 • The fir
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Example Overview Options FACTOR VAR
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Operations Example FACTOR 549 • V
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FACTOR 551 • By default, FACTOR u
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FACTOR 553 DET Determinant of the c
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• The factor pattern matrix is ro
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ROTATION Subcommand FACTOR 557 ROTA
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FACTOR 559 • The ROTATION subcomm
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Example Example Example Example FAC
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FACTOR 563 Jöreskog, K. G. 1977. F
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MULTIPUNCH Column binary file. IMAG
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FILE TYPE—END FILE TYPE For mixed
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Syntax Rules FILE TYPE—END FILE T
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Example * A grouped file of student
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FILE TYPE—END FILE TYPE 573 GROUP
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FILE TYPE—END FILE TYPE 575 • T
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FILE TYPE—END FILE TYPE 577 • T
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FILE TYPE—END FILE TYPE 579 WARN
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FILE TYPE—END FILE TYPE 581 BEGIN
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Example FILTER 583 • Both system-
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Example * A command file. FINISH 58
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Syntax Rules FIT 587 • If OBS is
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FLIP FLIP [[VARIABLES=] {ALL }] {va
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FLIP 591 Example Using the untransp
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FORMATS FORMATS varlist(format) [va
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Table 1 Output data formats (Contin
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FREQUENCIES FREQUENCIES [VARIABLES=
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Example FREQUENCIES 599 • FREQUEN
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FREQUENCIES 601 • MISSING and NOM
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Example FREQUENCIES 603 FREQUENCIES
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Example FREQUENCIES VAR=AGE /STATS=
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Options GENLOG 607 mates for unsatu
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GENLOG 609 • To separate the inde
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GENLOG 611 • For each effect, GEN
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GENLOG 613 DESIGN The design matrix
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GENLOG 615 • The saved variables
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GET GET FILE=file [/KEEP={ALL** }]
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GET 619 • Variables can be specif
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GET CAPTURE GET CAPTURE {ODBC }* [/
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GET CAPTURE 623 • You can display
Page 625 and 626: Overview GET DATA 625 GET DATA read
Page 627 and 628: READNAMES Subcommand GET DATA 627 O
Page 629 and 630: QUALIFIER Subcommand GET DATA 629 T
Page 631 and 632: GET SAS Overview Options GET SAS DA
Page 633 and 634: GET SAS 633 • Value labels read f
Page 635 and 636: GET TRANSLATE GET TRANSLATE FILE=fi
Page 637 and 638: GET TRANSLATE 637 • The first row
Page 639 and 640: GET TRANSLATE 639 • For string va
Page 641 and 642: GET TRANSLATE 641 • RANGE cannot
Page 643 and 644: GLM: Overview GLM is available in t
Page 645 and 646: GLM: Overview 645 • Display expec
Page 647 and 648: Model 2: Fixed-effects ANOVA and MA
Page 649 and 650: GLM: Overview 649 For univariate an
Page 651 and 652: GLM: Univariate GLM is available in
Page 653 and 654: Basic Specification GLM: Univariate
Page 655 and 656: GLM: Univariate 655 • If you spec
Page 657 and 658: INTERCEPT Subcommand GLM: Univariat
Page 659 and 660: GLM: Univariate 659 LOF Perform a l
Page 661 and 662: GLM: Univariate 661 • A coefficie
Page 663 and 664: Example GLM: Univariate 663 GLM DEP
Page 665 and 666: Example GLM: Univariate 665 GLM DEP
Page 667 and 668: GLM: Univariate 667 • Multiple PO
Page 669 and 670: COMPARE(factor) ADJ(method) GLM: Un
Page 671 and 672: DESIGN Subcommand GLM: Univariate 6
Page 673 and 674: Overview Options GLM: Multivariate
Page 675: GLM: Multivariate 675 Residual cova
Page 679 and 680: Subcommand Order • The list of de
Page 681 and 682: GLM: Repeated Measures 681 • Leve
Page 683 and 684: GLM: Repeated Measures 683 • WSFA
Page 685 and 686: GLM: Repeated Measures 685 • Thes
Page 687 and 688: GRAPH 687 The following table shows
Page 689 and 690: GRAPH 689 NGT(x) Number of cases fo
Page 691 and 692: • The default span (2) and sigma
Page 693 and 694: GRAPH 693 Figure 3 /BAR=MEAN(SALBEG
Page 695 and 696: GRAPH 695 Figure 9 /BAR(STACKED)=CO
Page 697 and 698: LINE Subcommand GRAPH 697 LINE crea
Page 699 and 700: Figure 19 /LINE=MEAN(THEFT AUTO BUR
Page 701 and 702: Figure 25 /LINE(AREA)=COUNT BY TIME
Page 703 and 704: Figure 31 LINE(DIFFERENCE)=VALUE(SA
Page 705 and 706: GRAPH 705 the variable must be dich
Page 707 and 708: ERRORBAR Subcommand GRAPH 707 ERROR
Page 709 and 710: GRAPH 709 MATRIX Scatterplot matrix
Page 711 and 712: Figure 49 /SCATTERPLOT(XYZ)=JOBCAT
Page 713 and 714: Figure 52 PARETO(CUM SIMPLE)=SUM(DE
Page 715 and 716: Elements and Attributes Dependent o
Page 717 and 718: HILOGLINEAR Overview HILOGLINEAR is
Page 719 and 720: Operations HILOGLINEAR 719 • HILO
Page 721 and 722: Example HILOGLINEAR 721 HILOGLINEAR
Page 723 and 724: Example * An Incomplete Rectangular
Page 725 and 726: HILOGLINEAR 725 NONE No plots. Spec
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HOMALS Overview Options HOMALS is a
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Example HOMALS 729 Values outside o
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HOMALS 731 The total number of vali
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Example HOMALS 733 HOMALS VARIABLES
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HOMALS 735 LEVEL String variable LE
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Basic Specification IF 737 The basi
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Example Example Example IF (AGE > 2
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Example Example IF 741 tions on eve
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{VERTICAL } {THREE } [/EFFECT={NONE
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Overview Options IGRAPH 745 The int
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CATORDER Subcommand IGRAPH 747 The
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Example IGRAPH /VIEWNAME=’Scatter
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CHARTLOOK Subcommand IGRAPH 751 CHA
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FORMAT Subcommand IGRAPH 753 For ch
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IGRAPH 755 The INTERPOLATE keyword
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IGRAPH 757 START num Indicates the
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Example IGRAPH /X1=VAR(region) TYPE
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ERRORBAR Subcommand IGRAPH 761 Erro
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IGRAPH 763 REGRESSION Fits a straig
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IGRAPH 765 Number of Cases Less Tha
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IMPORT IMPORT FILE=file [/TYPE={COM
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IMPORT 769 • DROP excludes a vari
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INCLUDE INCLUDE FILE=file Example O
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INFO This command is not available
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Types of Information The following
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INPUT PROGRAM—END INPUT PROGRAM I
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Example Example * Select cases with
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KEYED DATA LIST KEYED DATA LIST KEY
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Syntax Rules KEYED DATA LIST 783
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Example * Reading a keyed file: rea
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KM Overview KM is available in the
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KM 789 • Only one status variable
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STRATA Subcommand KM 791 STRATA ide
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TEST Subcommand KM 793 TEST specifi
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SAVE Subcommand KM 795 SAVE saves t
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Example LEAVE 797 • Numeric varia
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Operations Example Example LIST 799
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LIST 801 • If SPLIT FILE is in ef
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Temporary variables created by LOGI
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Limitations Example LOGISTIC REGRES
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LOGISTIC REGRESSION 807 DEVIATION(r
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LOGISTIC REGRESSION 809 Wald statis
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ID Subcommand LOGISTIC REGRESSION 8
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LOGISTIC REGRESSION 813 • POUT re
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LOGISTIC REGRESSION 815 • Assigne
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LOGLINEAR 817 multiway contingency
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Example Example Example Example LOG
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LOGLINEAR 821 • To enter cell cov
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CONTRAST Subcommand LOGLINEAR 823 C
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Example * Contrasts for a logistic
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Example LOGLINEAR RESPONSE(1,2) BY
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Example * Equiprobability model LOG
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Options LOOP—END LOOP 831 Missing
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LOOP—END LOOP 833 • The program
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LOOP—END LOOP 835 time, resulting
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LOOP—END LOOP 837 • The loop is
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MANOVA: Overview MANOVA is availabl
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Overview Example 2 * Analysis of Co
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MANOVA: Univariate MANOVA is availa
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MANOVA: Univariate 845 in the model
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MANOVA: Univariate 847 WITHIN Terms
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MANOVA: Univariate 849 The specifie
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MANOVA: Univariate 851 it generally
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PARAMETERS Keyword MANOVA: Univaria
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ERROR Keyword MANOVA: Univariate 85
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RESIDUALS Subcommand MANOVA: Univar
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MANOVA: Univariate 859 CELLPLOTS Ce
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Split Files and Variable Order MANO
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DESIGN Subcommand MANOVA: Univariat
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MANOVA: Univariate 865 • The numb
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MANOVA: Univariate 867 with similar
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MANOVA: Multivariate MANOVA is avai
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Limitations MANOVA: Multivariate 87
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MANOVA: Multivariate 873 DEVIATION
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MANOVA: Multivariate 875 • You mu
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MANOVA: Multivariate 877 SINGLEDF S
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MANOVA: Multivariate 879 ROTATE Rot
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CONDITIONAL and UNCONDITIONAL Keywo
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MANOVA: Repeated Measures 883 descr
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MANOVA: Repeated Measures 885 • I
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Example MANOVA: Repeated Measures 8
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MANOVA: Repeated Measures 889 • A
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1 MAPS MAPS {/GVAR = VAR(varname)[V
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Operations 893 MAPS • Each MAPS c
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895 MAPS • The total (sum) of sal
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Example 897 MAPS standard deviation
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Example 899 MAPS MAPS /GVAR = VAR(f
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Example 901 MAPS MAPS /GVAR = VAR(c
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Number of Cases Less Than (N of Cas
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906 MATCH FILES Variable Names. You
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908 MATCH FILES • If the files ha
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910 MATCH FILES RENAME Subcommand R
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912 MATCH FILES • FIRST creates a
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914 MATRIX—END MATRIX INV Inverse
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916 MATRIX—END MATRIX • A symme
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918 MATRIX—END MATRIX Example {11
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920 MATRIX—END MATRIX • All of
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922 MATRIX—END MATRIX ** &* &/ &*
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924 MATRIX—END MATRIX • The res
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926 MATRIX—END MATRIX COMPUTE Sta
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928 MATRIX—END MATRIX A = 111 111
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930 MATRIX—END MATRIX DET(M) Dete
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932 MATRIX—END MATRIX MOD(M,S) Re
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934 MATRIX—END MATRIX and the num
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936 MATRIX—END MATRIX • Constan
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938 MATRIX—END MATRIX • The fir
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940 MATRIX—END MATRIX • A logic
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942 MATRIX—END MATRIX • The mat
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944 MATRIX—END MATRIX FORMAT Spec
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946 MATRIX—END MATRIX HOLD Specif
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948 MATRIX—END MATRIX NAMES Speci
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950 MATRIX—END MATRIX • If more
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952 MATRIX—END MATRIX Valid keywo
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954 MATRIX—END MATRIX • The mat
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956 MATRIX—END MATRIX FNAMES Spec
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MATRIX DATA MATRIX DATA VARIABLES=v
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960 MATRIX DATA Table 1 summarizes
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962 MATRIX DATA Example • No othe
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964 MATRIX DATA Example * Matrix da
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966 MATRIX DATA • ROWTYPE_ is spe
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968 MATRIX DATA FULL do not enter t
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970 MATRIX DATA • The split varia
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972 MATRIX DATA Example MATRIX DATA
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974 MATRIX DATA Within-Cells Record
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976 MATRIX DATA • Because ROWTYPE
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978 MCONVERT • If there are multi
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MEANS Overview Options MEANS [TABLE
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982 MEANS • The first table list
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984 MEANS LINEARITY Test of lineari
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986 MISSING VALUES • For date for
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MIXED MIXED is available in the Adv
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990 MIXED Models The following are
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992 MIXED Basic Specification • T
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994 MIXED ARMA1 Autoregressive movi
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996 MIXED Example matrix: σ2 1 σ2
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998 MIXED Variable List The variabl
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1000 MIXED WITH (option) Covariate
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1002 MIXED • The following option
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1004 MIXED Otherwise, it will be ig
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1006 MIXED REGWGT Subcommand The RE
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1008 MIXED Example MIXED SCORE BY S
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1010 MIXED Interpretation of Random
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1012 MIXED
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1014 Syntax Reference Examples MODE
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1016 MRSETS These variables are cod
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1018 MRSETS DISPLAY Subcommand /DIS
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1020 MULT RESPONSE Options did not
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1022 MULT RESPONSE • The group na
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1024 MULT RESPONSE TABLES Subcomman
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1026 MULT RESPONSE ROW Row percenta
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1028 MULT RESPONSE The following ke
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1030 MVA Overview Options [/REGRESS
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1032 MVA Symbols The symbols displa
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1034 MVA Examples MVA VARIABLES=pop
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1036 MVA PERCENT=n Omit rows for va
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1038 MVA TPATTERN Subcommand The TP
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1040 MVA CONVERGENCE=value Converge
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1042 MVA ADDTYPE Type of distributi
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NLR NLR and CNLR are available in t
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1046 NLR Basic Specification The ba
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1048 NLR • The two COMPUTE statem
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1050 NLR Example MODEL PROGRAM A=1,
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1052 NLR • Some new parameters ma
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1054 NLR Example MODEL PROGRAM A=.5
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1056 NLR sponding to a more accurat
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1058 NLR Nonlinear Constraints Nonl
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1060 NLR • CNLR generates the boo
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1062 N OF CASES Example GET FILE=CI
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1064 NOMREG Overview Options NOMREG
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1066 NOMREG mates convergence crite
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1068 NOMREG B are factors. The expr
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1070 NOMREG • The initial model c
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1072 NOMREG • Only one OUTFILE su
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1074 NOMREG SCALE Subcommand The SC
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NONPAR CORR Overview Options NONPAR
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1078 NONPAR CORR Example NONPAR COR
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1080 NONPAR CORR Format of the Matr
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NPAR TESTS NPAR TESTS [CHISQUARE=va
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1084 NPAR TESTS Options Statistical
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1086 NPAR TESTS • If more than 0.
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1088 NPAR TESTS Operations Example
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1090 NPAR TESTS Example • A test
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1094 NPAR TESTS MEDIAN Subcommand S
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1096 NPAR TESTS MOSES Subcommand Sy
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1100 NPAR TESTS Example • Under t
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NUMERIC NUMERIC varlist[(format)] [
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OLAP CUBES OLAP CUBES {varlist} BY
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1106 OLAP CUBES • A Layered Repor
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1108 OLAP CUBES The percentage diff
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OMS Note: Square brackets used in t
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1112 OMS bles from the FREQUENCIES
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1114 OMS Example OMS /SELECT TABLES
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1116 OMS be followed by an equals s
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1118 OMS SVWSOXML XML used by Smart
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1120 OMS • The first OMS command
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1122 OMS keyword must be followed b
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1124 OMS Figure 4 Single two-dimens
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1126 OMS Figure 6 Multiple Tables w
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1128 OMS Figure 8 Tables with diffe
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1130 OMS tics in the columns. To in
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1132 OMS Figure 11 Variable names i
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1134 OMS Figure 13 OXML for simple
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1136 OMS Command and Subtype Identi
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1138 OMS Table 2 Command Command Id
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OMSINFO OMSINFO. Example OMSINFO. O
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13 1156 OMSEND OMSEND Note: Square
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OMSLOG OMSLOG FILE = ’filespec’
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ONEWAY Overview Options ONEWAY varl
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1162 ONEWAY Analysis List The analy
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1164 ONEWAY groups. In addition, ho
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1166 ONEWAY • Up to 10 RANGE subc
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1168 ONEWAY • The dependent varia
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ORTHOPLAN Overview Options ORTHOPLA
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1172 ORTHOPLAN Example • The FACT
Page 1174 and 1175:
1174 ORTHOPLAN • MINIMUM is follo
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1176 Syntax Reference Basic Specifi
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1178 Syntax Reference SETS Subcomma
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1180 Syntax Reference CONVERGENCE S
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1182 Syntax Reference SAVE Subcomma
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PACF Overview Options PACF [VARIABL
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PACF 1187 • DIFF differences the
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PACF 1189 • If SEASONAL is specif
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PARTIAL CORR Overview Options PARTI
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VARIABLES Subcommand PARTIAL CORR 1
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FORMAT Subcommand FORMAT determines
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Format of the Matrix Data File Spli
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PARTIAL CORR 1199 • REGRESSION co
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PERMISSIONS PERMISSIONS FILE=’fil
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PLANCARDS Overview Options PLANCARD
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Example PLANCARDS 1205 DATA LIST FR
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Figure 2 Single-profile format Prof
Page 1209:
Profile # 1 Circle the number in th
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1212 PLUM Basic Specification The b
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1214 PLUM value. The criterion is n
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1216 PLUM CELLINFO Cell Information
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1218 PLUM • Covariates can be con
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POINT POINT KEY=varname [FILE=file]
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1222 POINT Example • DATA LIST re
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PPLOT PPLOT [VARIABLES=] varlist [/
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1226 PPLOT Operations • Subcomman
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1228 PPLOT FRACTION Subcommand FRAC
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1230 PPLOT • In low resolution, a
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1232 PPLOT • If a natural log tra
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PREDICT PREDICT [{start date }] [TH
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1236 PREDICT • If there is a gap
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PRESERVE PRESERVE Overview PRESERVE
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1240 Syntax Reference Basic Specifi
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1242 Syntax Reference Example DATA
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1244 Syntax Reference CONVERGENCE S
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1248 Syntax Reference VARNAME_ Stri
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1250 PRINT Syntax Rules • A slash
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1252 PRINT Strings You can specify
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1254 PRINT Example PRINT OUTFILE=PR
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1256 PRINT EJECT Basic Specificatio
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PRINT FORMATS PRINT FORMATS varlist
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1260 PRINT FORMATS Example COMPUTE
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1262 PRINT SPACE Example PRINT / NA
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PROBIT Overview Options PROBIT is a
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1266 PROBIT • If individual cases
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1268 PROBIT • Keywords BY and WIT
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1270 PROBIT mately six significant
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1272 PROBIT RMP Relative median pot
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1274 PROCEDURE OUTPUT Example PROCE
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1276 PROXIMITIES Example Overview O
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1278 PROXIMITIES RANGE Standardize
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1280 PROXIMITIES CORRELATION( xy ,
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1282 PROXIMITIES 0, 1, 1, 0, 0 for
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1284 PROXIMITIES K1[(p[,np])] Kulcz
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1286 PROXIMITIES SS5[(p[,np])] Soka
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1288 PROXIMITIES • When used with
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1290 PROXIMITIES analysis. If a var
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1292 PROXIMITIES Example Example GE
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1294 PROXIMITIES
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1296 Syntax Reference Overview Opti
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1302 Syntax Reference TORGERSON Tor
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1304 Syntax Reference SPLINE Monoto
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1306 Syntax Reference number of cas
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1308 Syntax Reference DIFFSTRESS Co
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1310 Syntax Reference PLOT Subcomma
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1312 Syntax Reference cases in the
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1314 QUICK CLUSTER distances betwee
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1316 QUICK CLUSTER is specified, QU
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1318 QUICK CLUSTER NONE No addition
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RANK RANK [VARIABLES=] varlist [({A
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1322 RANK • String variables cann
Page 1324 and 1325:
1324 RANK TIES Subcommand TIES dete
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RATIO STATISTICS RATIO STATISTICS n
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1328 RATIO STATISTICS • Keywords
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1330 RATIO STATISTICS MEAN Mean. Th
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1332 Syntax Reference Syntax Rules
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RECODE For numeric variables: RECOD
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1336 RECODE • SYSMIS specifies th
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1338 RECODE • Values for V1 throu
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RECORD TYPE For mixed file types: R
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1342 RECORD TYPE Example * Reading
Page 1344 and 1345:
1344 RECORD TYPE Example * A mixed
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1346 RECORD TYPE Example * Specifyi
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1348 RECORD TYPE • SPREAD can be
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1350 REFORMAT Example * Convert an
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1352 REGRESSION Overview Options RE
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1354 REGRESSION • The DEPENDENT s
Page 1356 and 1357:
1356 REGRESSION • When more than
Page 1358 and 1359:
1358 REGRESSION Equation Statistics
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1360 REGRESSION PIN[(value)] Probab
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1362 REGRESSION • COMPUTE creates
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1364 REGRESSION • If SELECT is sp
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1366 REGRESSION MISSING Subcommand
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1368 REGRESSION: Residuals (RESIDUA
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1370 REGRESSION: Residuals • REGR
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1372 REGRESSION: Residuals are RESI
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1374 REGRESSION: Residuals Example
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RELIABILITY Overview RELIABILITY VA
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1378 RELIABILITY • This example a
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1380 RELIABILITY ICC Subcommand ICC
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1382 RELIABILITY Matrix Output Matr
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1384 RELIABILITY Example Example Ex
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RENAME VARIABLES RENAME VARIABLES {
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REPEATING DATA REPEATING DATA [FILE
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1390 REPEATING DATA • The DATA LI
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1392 REPEATING DATA Example Figure
Page 1394 and 1395:
1394 REPEATING DATA STARTS Subcomma
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1396 REPEATING DATA • In the data
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1398 REPEATING DATA CONTINUED Subco
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1400 REPEATING DATA in positions 1-
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REPORT REPORT [/FORMAT=[{MANUAL }]
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1404 REPORT Column Widths. Default
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1406 REPORT statistics. The maximum
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1408 REPORT Example • The maximum
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1410 REPORT headings with the first
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1412 REPORT Figure 1 Page layout fo
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1414 REPORT Example REPORT FORMAT=A
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1416 REPORT Column Format The follo
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1418 REPORT BREAK Subcommand BREAK
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1420 REPORT (OFFSET) Position of th
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1422 REPORT • To use the summary
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1424 REPORT means of all three vari
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1426 REPORT • The summary title m
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1428 REPORT Table 3 Default print f
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1430 REPORT • The right-justified
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1432 REREAD Subcommand Order Subcom
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1434 REREAD FILE Subcommand FILE sp
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1436 REREAD Example * Reading both
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RMV RMV new variables={LINT (varlis
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1440 RMV • NEWVAR1 will have the
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ROC Overview Options ROC varlist BY
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1444 ROC MISSING Subcommand Cases w
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SAMPLE SAMPLE {decimal value} {n FR
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SAVE SAVE OUTFILE=’filespec’ [/
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1450 SAVE Example GET FILE=HUBEMPL.
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1452 SAVE Example GET FILE=PRSNL. C
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1454 SAVE PERMISSIONS Subcommand Th
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1456 Syntax Reference SAVE MODEL Ov
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1458 Syntax Reference TYPE Subcomma
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1460 SAVE TRANSLATE ** Required for
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1462 SAVE TRANSLATE • User-missin
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1464 SAVE TRANSLATE The following t
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1466 SAVE TRANSLATE WK1 1-2-3 Relea
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1468 SAVE TRANSLATE CELLS Subcomman
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1470 SAVE TRANSLATE Example SAVE TR
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1472 SCRIPT
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1474 Syntax Reference Subcommand Or
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1476 Syntax Reference PERIOD Subcom
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SELECT IF SELECT IF [(]logical expr
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1480 SELECT IF Missing Values • I
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1482 SELECT IF Example Example Exam
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1484 SET [HEADER={NO** }] {YES } {B
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1486 SET Operations Example • Set
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1488 SET LABELS Displays variable l
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1490 SET JOURNAL Subcommand Alias t
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1492 SET SPSSB in the server versio
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1494 SET • You can specify any le
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1496 SET Example SET SMALL = 0. SET
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SHOW SHOW [ALL] [BLANKS] [BLKSIZE]
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1500 SHOW EPOCH Range of years for
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1502 SHOW WEIGHT Variable used to w
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1504 SORT CASES addition, special c
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1506 SPCHART Overview Options SPCHA
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1508 SPCHART • The default span (
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1510 SPCHART Figure 3 S chart Data
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1512 SPCHART Figure 4 Individuals c
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1514 SPCHART Figure 7 NP chart Data
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1516 SPCHART based on the Poisson d
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1518 SPCHART • A duplicated subco
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1520 SPCHART CAPSIGMA Subcommand Th
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1522 SPCHART fixed control limits a
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1524 Syntax Reference SPECTRA Overv
Page 1526 and 1527:
1526 Syntax Reference Syntax Rules
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1528 Syntax Reference TUKEY Tukey-H
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Page 1532 and 1533:
1532 Syntax Reference References
Page 1534 and 1535:
1534 SPLIT FILE • AGGREGATE ignor
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STRING STRING varlist (An) [/varlis
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SUBTITLE SUBTITLE [’]text[’] Ex
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SUMMARIZE SUMMARIZE [TABLES=]{varli
Page 1542 and 1543:
1542 SUMMARIZE Example the FORMAT s
Page 1544 and 1545:
1544 SUMMARIZE GEOMETRIC Geometric
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SURVIVAL Overview SURVIVAL is avail
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1548 SURVIVAL Operations • SURVIV
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1550 SURVIVAL • INTERVALS produce
Page 1552 and 1553:
1552 SURVIVAL HAZARD Plot the hazar
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1554 SURVIVAL large samples. It is
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1556 SURVIVAL WRITE Subcommand Form
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1558 SURVIVAL • Record type 31 is
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1560 SYSFILE INFO
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1562 Syntax Reference Operations Ex
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1564 TEMPORARY Example • The XSAV
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TITLE TITLE [’]text[’] Example
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TSET TSET [PRINT={DEFAULT**}] [/NEW
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1570 TSET MXPREDICT Subcommand MXPR
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TSPLOT TSPLOT [VARIABLES=] variable
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1574 TSPLOT Subcommand Order • Su
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1576 TSPLOT Example TSPLOT TICKETS
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1578 TSPLOT Figure 3 FORMAT=REFEREN
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1580 TSPLOT UNIFORM Scale uniformly
Page 1582 and 1583:
1582 T-TEST degrees of freedom, and
Page 1584 and 1585:
1584 T-TEST PAIRS Subcommand PAIRS
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2SLS Overview Options 2SLS is avail
Page 1588 and 1589:
1588 2SLS • In this example, Y1 i
Page 1590 and 1591:
1590 2SLS • In this example, the
Page 1592 and 1593:
TWOSTEP CLUSTER Overview TWOSTEP CL
Page 1594 and 1595:
1594 TWOSTEP CLUSTER Example TWOSTE
Page 1596 and 1597:
1596 TWOSTEP CLUSTER Example TWOSTE
Page 1598 and 1599:
1598 TWOSTEP CLUSTER STATE Save the
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1600 TWOSTEP CLUSTER SUMMARY Descri
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1602 UNIANOVA Overview Options ** D
Page 1604 and 1605:
1604 UNIANOVA Example UNIANOVA YIEL
Page 1606 and 1607:
1606 UNIANOVA appropriate effect is
Page 1608 and 1609:
1608 UNIANOVA PRINT Subcommand PRIN
Page 1610 and 1611:
1610 UNIANOVA Example UNIANOVA DEP
Page 1612 and 1613:
1612 UNIANOVA • B1 versus B2 at A
Page 1614 and 1615:
1614 UNIANOVA HELMERT Helmert contr
Page 1616 and 1617:
1616 UNIANOVA Post Hoc Tests Statis
Page 1618 and 1619:
1618 UNIANOVA QREGW Ryan-Einot-Gabr
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1620 UNIANOVA SRESID Studentized re
Page 1622 and 1623:
UPDATE UPDATE FILE={master file} {*
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1624 UPDATE • UPDATE copies all v
Page 1626 and 1627:
1626 UPDATE BY Subcommand BY specif
Page 1628 and 1629:
1628 UPDATE • IN has only one spe
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1630 USE • Values on keyword YEAR
Page 1632 and 1633:
1632 USE USE THRU 5. • This comma
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1634 VALUE LABELS Operations Exampl
Page 1636 and 1637:
VARCOMP Overview Options VARCOMP is
Page 1638 and 1639:
1638 VARCOMP • You can specify mu
Page 1640 and 1641:
1640 VARCOMP The default is 1.0E-8.
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1642 VARCOMP Nesting within an inte
Page 1644 and 1645:
VARIABLE LABELS VARIABLE LABELS var
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VARIABLE LEVEL VARIABLE LEVEL varli
Page 1648 and 1649:
VARSTOCASES VARSTOCASES /MAKE new v
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1650 VARSTOCASES The command: VARST
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1652 VARSTOCASES Example VARSTOCASE
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1654 VARSTOCASES COUNT Subcommand W
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1656 VECTOR Syntax Rules • Multip
Page 1658 and 1659:
1658 VECTOR Example VECTOR X,Y(A5,6
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1660 VECTOR • Aggregating by vari
Page 1662 and 1663:
1662 VERIFY Operations • VERIFY r
Page 1664 and 1665:
1664 WEIGHT Example • Each WEIGHT
Page 1666 and 1667:
1666 WLS without erasing values sav
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1668 WLS DELTA Subcommand DELTA, al
Page 1670 and 1671:
1670 WLS Example WLS X1 WITH Y1 /SO
Page 1672 and 1673:
1672 WRITE Basic Specification The
Page 1674 and 1675:
1674 WRITE Strings • MOHIRED, YRH
Page 1676 and 1677:
WRITE FORMATS WRITE FORMATS varlist
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1678 WRITE FORMATS Example COMPUTE
Page 1680 and 1681:
1680 XSAVE Basic Specification The
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1682 XSAVE • Keyword ALL on KEEP
Page 1684:
1684 XSAVE • The only specificati
Page 1687 and 1688:
Appendix A Commands and Program Sta
Page 1689 and 1690:
Determining Command Order Commands
Page 1691 and 1692:
Table A.1 Commands and program stat
Page 1693 and 1694:
Input Program Commands Commands and
Page 1695 and 1696:
Appendix B IMPORT/EXPORT Character
Page 1697 and 1698:
Position Graphic Macintosh Microsof
Page 1699 and 1700:
Position Graphic Macintosh Microsof
Page 1701:
Position Graphic Macintosh 222 â 1
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1704 Appendix C Figure C.1 File REC
Page 1706 and 1707:
1706 Appendix C Figure C.4 NESTED.D
Page 1708 and 1709:
1708 Appendix C • The output from
Page 1710 and 1711:
1710 Appendix C FILE TYPE GROUPED R
Page 1712 and 1713:
1712 Appendix C Figure C.8 LIST out
Page 1714 and 1715:
1714 Appendix C • REPEATING DATA
Page 1716 and 1717:
1716 Appendix C The loop terminates
Page 1718 and 1719:
1718 Appendix D Example 1: Automati
Page 1720 and 1721:
1720 Appendix D • Commands betwee
Page 1722 and 1723:
1722 Appendix D Figure D.7 Listing
Page 1724 and 1725:
1724 Appendix D You can modify the
Page 1726 and 1727:
1726 Appendix D • !TOKENS(1) spec
Page 1728 and 1729:
1728 Appendix D Figure D.15 Output
Page 1730 and 1731:
1730 Appendix D Figure D.19 !DATAGE
Page 1733 and 1734:
Appendix A Trends Options: Special
Page 1735 and 1736:
Trends Options: Special Considerati
Page 1737 and 1738:
Trends Options: Special Considerati
Page 1739 and 1740:
Subject Index active data file cach
Page 1741 and 1742:
inary shape difference in Distances
Page 1743 and 1744:
in Logistic Regression procedure, 8
Page 1745 and 1746:
in Multidimensional Scaling procedu
Page 1747 and 1748:
scratch files, 1492 data dictionary
Page 1749 and 1750:
matrices, 479 matrix input, 482 mat
Page 1751 and 1752:
in Hierarchical Loglinear Analysis
Page 1753 and 1754:
cell weights, 610, 821 contrasts, 8
Page 1755 and 1756:
hierarchical loglinear analysis, 71
Page 1757 and 1758:
in Autoregression procedure, 148- 1
Page 1759 and 1760:
See also Linear Regression procedur
Page 1761 and 1762:
linear transformations, 871 naming
Page 1763 and 1764:
date format variables, 986 defining
Page 1765 and 1766:
function precision, 1056 infinite s
Page 1767 and 1768:
in Cox Regression procedure, 302 in
Page 1769 and 1770:
output, 1489, 1494 output page size
Page 1771 and 1772:
elative risk ratio in Crosstabs pro
Page 1773 and 1774:
scientific notation controlling dis
Page 1775 and 1776:
in ROC Curve procedure, 1445 standa
Page 1777 and 1778:
fixed format, 414, 415, 416, 423 fr
Page 1779 and 1780:
date format variables, 1633 in Homo
Page 1781 and 1782:
Syntax Index !BLANKS (function) DEF
Page 1783 and 1784:
AGGREGATE (command), 90 BREAK subco
Page 1785 and 1786:
AR (subcommand) ARIMA command, 158
Page 1787 and 1788:
REPORT command, 1417 BLANKS (subcom
Page 1789 and 1790:
CASENUM$ (system variable) in PRINT
Page 1791 and 1792:
REGRESSION command, 1360 CIN (subco
Page 1793 and 1794:
SHOW command, 1499 COMPUTE (command
Page 1795 and 1796:
TWOSTEP CLUSTER command, 1600 COUNT
Page 1797 and 1798:
WRITE subcommand, 325 CROSSVALID (k
Page 1799 and 1800:
Symbols D (keyword) CLUSTER command
Page 1801 and 1802:
NPAR TESTS command, 1100 ONEWAY com
Page 1803 and 1804:
DISPER (keyword) CLUSTER command, 2
Page 1805 and 1806:
MIXED command, 999 UNIANOVA command
Page 1807 and 1808:
EXPERIMENTAL (keyword) ANOVA comman
Page 1809 and 1810:
MEANS command, 983 PROXSCAL command
Page 1811 and 1812:
GAC (keyword) OLAP CUBES command, 1
Page 1813 and 1814:
TEST(SSCP) keyword, 674 TUKEY keywo
Page 1815 and 1816:
HISTORY (keyword) CATPCA command, 2
Page 1817 and 1818:
INCLUDE (command), 771 FILE subcomm
Page 1819 and 1820:
JITTER (keyword) IGRAPH command, 75
Page 1821 and 1822:
LEAVE (command), 796 LEFT (keyword)
Page 1823 and 1824:
CWEIGHT subcommand, 821 DESIGN subc
Page 1825 and 1826:
ROVMAP subcommand, 896 SHOWLABEL su
Page 1827 and 1828:
RMV command, 1440 MEAN (keyword) AN
Page 1829 and 1830:
MANOVA command, 858 MEANS command,
Page 1831 and 1832:
CATREG command, 212 MULTIPUNCH (key
Page 1833 and 1834:
NMISS (function) AGGREGATE command,
Page 1835 and 1836:
NOREFERENCE (keyword) TSPLOT comman
Page 1837 and 1838:
CATREG command, 215 ODBC (keyword)
Page 1839 and 1840:
RATIO STATISTICS command, 1328 REGR
Page 1841 and 1842:
PER (keyword) SPECTRA command, 1530
Page 1843 and 1844:
SUMMARY keyword, 1216 TEST subcomma
Page 1845 and 1846:
CATREG command, 215 CLUSTER command
Page 1847 and 1848:
QUANT (keyword) CATPCA command, 200
Page 1849 and 1850:
with FORMATS command, 1349 REG (key
Page 1851 and 1852:
CSTABULATE command, 374 CURVEFIT co
Page 1853 and 1854:
SAMPLEFILE (subcommand) CSSELECT co
Page 1855 and 1856:
SEED (keyword) CSSELECT command, 34
Page 1857 and 1858:
MXLOOPS subcommand, 1500 MXMEMORY s
Page 1859 and 1860:
SP (subcommand) ARIMA command, 156-
Page 1861 and 1862:
DISCRIMINANT command, 479 FREQUENCI
Page 1863 and 1864:
Symbols T (function) MATRIX command
Page 1865 and 1866:
TOLERANCE (subcommand) DISCRIMINANT
Page 1867 and 1868:
UNCLASSIFIED (keyword) DISCRIMINANT
Page 1869 and 1870:
REPORT command, 1414, 1418 value la
Page 1871 and 1872:
VIN (subcommand) DISCRIMINANT comma
Page 1873 and 1874:
X1MULTIPLIER (keyword) IGRAPH comma
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SPSS® 12.0 Command Syntax Reference

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