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1 IBM SPSS Statistics Base 22
2 195 IBM SPSS Statistics IBM SPSS Statistics Base 22
3 DESCRIPTIVES EXAMINE OLAP OLAP OLAP OLAP t t T t.. 34 t T t T-TEST T t T-TEST T-TEST ONEWAY GLM GLM GLM GLM GLM UNIANOVA GLM GLM UNIANOVA GLM GLM UNIANOVA CORRELATIONS NONPAR CORR PARTIAL CORR PROXIMITIES iii
4 : REGRESSION PLUM k k : : : : : : DISCRIMINANT FACTOR TwoStep TwoStep TwoStep CLUSTER QUICK CLUSTER NPTESTS NPTESTS i IBM SPSS Statistics Base 22
5 NPTESTS NPTESTS Kolmogoro-Smirno MULT RESPONSE : / / : REPORT RELIABILITY ALSCAL ROC ROC
6 i IBM SPSS Statistics Base 22
7 1 () 4 : ( ) 1. > > () 3 ( ) : : Copyright IBM Corp. 1989,
8 : : ( A4 F8.2 DATE11 ) : : ( ) : : : : : $@ : IBM SPSS Statistics IBM SPSS Statistics ( ) : IBM SPSS Statistics ( ) IBM SPSS Statistics : : FILE LABEL ( ) 2 IBM SPSS Statistics Base 22
9 : : : DATAFILE ATTRIBUTE : $@ : : () : 4 ( ) : ( ) : : 200 : ( ) Percent ( ). 1 3
10 Mean (). () Standard Deiation (). 1 68% 2 95% % Quartiles (4 (Kaplan-Meier)) : ( ) 4 IBM SPSS Statistics Base 22
11 2 ( ) : 37.5% 24.9% 28.1% 9.4% 3,576 1,078 : () 4 : ( ) : 4 1. > > : 4 ( ) 4 4 n ( 95 95% ) Copyright IBM Corp. 1989,
12 : Mean (). () Median ( ) : Sum (). () : : 1 68% 2 95% % Variance ( ( )). 1 2 Range (OK ( )). Minimum ( ). Maximum (). : ( ) : Skewness (). 0 2 Kurtosis (). 0 : (30 35 ) 6 IBM SPSS Statistics Base 22
13 : 1 1 : : ( ) : 1 ( ) ( ) : 2 7
14 8 IBM SPSS Statistics Base 22
15 3 1 1 (z ) ( ) ( ) z (1 ) z : ( Bob Kim Brian 1 ) : : ( ) : (z ) ( ) z z 1. > > z : () : Copyright IBM Corp. 1989,
16 : 1 68% 2 95% % Variance ( ( )). 1 2 Range (OK ( )). Minimum ( ). Maximum (). : ( ) : Kurtosis (). 0 Skewness (). 0 2 : DESCRIPTIVES (z ) (VARIABLES ) (VARIABLES ) (MISSING ) (SORT ) 10 IBM SPSS Statistics Base 22
17 4 () : : 5% 4 ( ) Huber M Andrews Hampel M Tukey 5 5 Lilliefors Kolmogoro-Smirno Shapiro-Wilk Leene : () () ( ) ( 15 ) : 1. > > ( ) Leene Copyright IBM Corp. 1989,
18 : 5% 4 95% M : Huber M Andrews Hampel M Tukey : 5 5 : : 1 1 : : Lilliefors Kolmogoro-Smirno 3 50 Shapiro-Wilk 3 5,000 Leene : Leene Leene 4 ( ) 4 1 : 12 IBM SPSS Statistics Base 22
19 : : : 2 : 2 3 : 3 : : : () : EXAMINE EXAMINE (TOTAL ) (SCALE ) (VARIABLES ) (PERCENTILES ) 5 (PERCENTILES ) (PLOT ) (STATISTICS ) M (MESTIMATORS ) 4 13
20 14 IBM SPSS Statistics Base 22
21 ( ) ( 2 ) 1 ( ) ( ) 2 2 : ( 500 ) ( 2,500 ) : Pearson 2 2 Fisher Yates 2 Pearson r Spearman Cramér V Goodman Kruskal Somers d Kendall b Kendall c Cohen McNemar Cochran Mantel-Haenszel : (8 ) 1 2 : ( ) ( ) ( ) 2 (Cramer V ) : ( : 1=2 =3 = ) 1. > > Copyright IBM Corp. 1989,
22 1 2 1 () (3 ) 2 : : () demo.sa ( Samples ) 1. ( ) [ ] [] [] : Pearson 2 2 Fisher Yates 2 ( ) Fisher 2 2 Yates 2 16 IBM SPSS Statistics Base 22
23 2 Pearson : Spearman ( ) Spearman () Pearson r : ( ) () Cramér V ( Goodman Kruskal ) : Phi and Cramer's V ( ()). 2 2 Cramer V 2 Lambda ( ). 1 0 : % : ( )Kendall bkendall c Somers d Gamma ( ) n Somers' d (Somers d) ( ) Somers d Kendall's tau-b (Kendall b). () Kendall's tau-c (Kendall c) : 5 17
24 : () () Kappa ( ()). Cohen ( ) : 2x2 1 McNemar: 2 2 McNemar-Bowker Cochran's and Mantel-Haenszel statistics (Cochran Mantel-Haenszel ). Cochran Mantel-Haenszel 1 () 2 2 Cochran Mantel-Haenszel 2 3 () : ( ) <NN 2 0 : () APA 0.05 : APA p (Bonferroni ): Bonferroni : (1 ) : 18 IBM SPSS Statistics Base 22
25 : 2 : Pearson 0 1 : ( - ) : (1.25 ) Round cell counts (). Truncate cell counts (). Round case weights ( ). Truncate case weights ( ). : ( ) 5 19
26 20 IBM SPSS Statistics Base 22
27 6 1 n : : N N : : () 1. > > n n Copyright IBM Corp. 1989,
28 N N 1 First (). Geometric Mean (). n n Grouped Median ( ) Harmonic Mean (). Kurtosis (). 0 Last (). Maximum (). Mean (). () Median ( ) Minimum ( ). N. () : : 22 IBM SPSS Statistics Base 22
29 Range (OK ( )). Skewness (). 0 2 Standard Deiation (). 1 68% 2 95% % Standard Error of Kurtosis ( ). ( ) () Standard Error of Mean (). ( ) Standard Error of Skewness ( ). ( ) Sum (). () Variance ( ( ))
30 24 IBM SPSS Statistics Base 22
31 7 1 1 : 3 : N N 2 R R 2 : : ( ) Leene 1. > > R R 2 N Copyright IBM Corp. 1989,
32 N 1 First (). Geometric Mean (). n n Grouped Median ( ) Harmonic Mean (). Kurtosis (). 0 Last (). Maximum (). Mean (). () Median ( ) Minimum ( ). N. () Percent of total N (). Percent of total sum ( ). Range (OK ( )). Skewness (). 0 2 Standard Deiation (). 1 68% 2 95% % IBM SPSS Statistics Base 22
33 Standard Error of Kurtosis ( ). ( ) () Standard Error of Mean (). ( ) Standard Error of Skewness ( ). ( ) Sum (). () Variance ( ( )) : 2 ( ) : F R R2 7 27
34 28 IBM SPSS Statistics Base 22
35 8 OLAP OLAP (Online Analytical Processing) 1 1 : : OLAP : () : () OLAP 1. > > OLAP ( ) 1 ( ) ( ) <NN 2 Copyright IBM Corp. 1989,
36 OLAP 1 1 First (). Geometric Mean (). n n Grouped Median ( ) Harmonic Mean (). Kurtosis (). 0 Last (). Maximum (). Mean (). () Median ( ) Minimum ( ). N. () Percent of N in ( ). 1 : 1 : 30 IBM SPSS Statistics Base 22
37 : Range (OK ( )). Skewness (). 0 2 Standard Deiation (). 1 68% 2 95% % Standard Error of Kurtosis ( ). ( ) () Standard Error of Mean (). ( ) Standard Error of Skewness ( ). ( ) Sum (). () Variance ( ( )). 1 2 OLAP OLAP : : 2 () 2 : 2 () 1 8 OLAP 31
38 OLAP n 32 IBM SPSS Statistics Base 22
39 9 t t 3 t t (2 t ): 2 1 Leene t 95% t ( t ): 1 2 t 95% 1 t : 1 t 95% T t 2 2 ( ) () : 2 2 t 1 : : : ( ) : Leene 2 t t : ( ) () 2 ( ) Copyright IBM Corp. 1989,
40 : t t 2 t t 1. > > t t t 2 t 2 : 1 2 ( ) : () t : 95% 1 99 : 1 () : t : t t T t : 2 34 IBM SPSS Statistics Base 22
41 (75 75 ) : : : t ( ) t : 2 ( ) : t 1. > > t t : 95% 1 99 : 1 () : t : t T-TEST 1 t 1 t (PAIRS ) 1 T 1 t 9 t 35
42 : IQ % 1.3 : : 0 t ( ) 1 t : : 1 t 1. > > 1 t t : 95% 1 99 : 1 () : t : t t T-TEST 1 t 1 t (PAIRS ) T-TEST 1 t 1 t (PAIRS ) 36 IBM SPSS Statistics Base 22
43 9 t 37
44 38 IBM SPSS Statistics Base 22
45 10 () 2 t 2 : 3 : 95% Leene : Bonferroni Sidak Tukey HSD Hochberg GT2 Gabriel Dunnett Ryan-Einot-Gabriel-Welsch F (R-E-G-W F) Ryan-Einot-Gabriel-Welsch (R-E-G-W Q) Tamhane T2 Dunnett T3 Games-Howell Dunnett C Duncan Student-Newman-Keuls (S-N-K) Tukey b Waller-Duncan Scheffé : () : Leene 1. > > Copyright IBM Corp. 1989,
46 ( ) : : t ( ) Tukey HSD Hochberg GT2 Gabriel Scheffé Tukey b S-N-K (Student-Newman-Keuls) Duncan R-E-G-W F (Ryan-Einot-Gabriel-Welsch F ) R-E-G-W Q (Ryan-Einot-Gabriel-Welsch ) Waller-Duncan Bonferroni Tukey HSD Sidak Gabriel Hochberg Dunnett Scheffé LSD ( ) LSD: t Bonferroni. t Sidak: t Sidak Bonferroni Scheffe. F R-E-G-W F: F Ryan-Einot-Gabriel-Welsch R-E-G-W Q: Ryan-Einot-Gabriel-Welsch S-N-K: 40 IBM SPSS Statistics Base 22
47 Tukey: Tukey b: Tukey HSD Student-Newman-Keuls Duncan: Student Newman Keuls Hochberg GT2: Tukey HSD Gabriel: Hochberg GT2 Gabriel Waller-Duncan: t Dunnett: t () < > Tamhane T2 Dunnett T3 Games-Howell Dunnett C Tamhane T2: t Dunnett T3: Games-Howell: Dunnett C: : ( ) : 1 : 95% : 95% 95% 10 41
48 : Leene Brown-Forsythe: Brown-Forsythe F Welch: Welch F : ( ) : : : ONEWAY 95% 95% (STATISTICS=EFFECTS ) Bonferroni Duncan Scheffé (RANGES ) (MATRIX ) 42 IBM SPSS Statistics Base 22
49 11 GLM 1 GLM () GLM 1 F () Cook WLS (WLS) : () : I II III IV III : : Bonferroni Sidak Scheffé Ryan-Einot-Gabriel-Welsch F Ryan-Einot-Gabriel-Welsch Student-Newman-Keuls Tukey HSD Tukey b Duncan Hochberg GT2 Gabriel Waller-Duncan t Dunnett ( ) Tamhane T2 Dunnett T3 Games-Howell Dunnett C : Leene : () GLM 1 : 8 Copyright IBM Corp. 1989,
50 : GLM 1 1. > > WLS 0 GLM 1. 1 : : : 44 IBM SPSS Statistics Base 22
51 : III : : : 2 : 2 3 : 3 4 : 4 5 : 5 III I: I ( ) ( ) ( ) II: II ( ) III: () III 11 GLM 1 45
52 Yates 2 III I II IV: F F IV = III = II F IV F IV I II GLM 1 ( ) GLM 1 : LB =0 L B L L L F t Bonferroni Helmert () ( ) ( ) ( Helmert ) Helmert ( ) ( ) ( 3 ) 46 IBM SPSS Statistics Base 22
53 GLM () 1 1 ( ) 2 3 Adanced Statistics GLM GLM ( ) ( ) GLM : : : (LSD) Bonferroni Sidak : 2 2 t L 11 GLM 1 47
54 Leene : UNIANOVA (DESIGN ) () (TEST ) (CONTRAST ) (MISSING ) EPS (CRITERIA ) L M K (LMATRIX MMATRIX KMATRIX ) (CONTRAST ) (CONTRAST ) (POSTHOC ) (EMMEANS ) (SAVE ) (OUTFILE ) (OUTFILE ) (OUTFILE ) GLM : GLM GLM Adanced Statistics GLM GLM 48 IBM SPSS Statistics Base 22
55 Bonferroni Tukey HSD Bonferroni t Sidak t Bonferroni Tukey HSD Tukey HSD Bonferroni Bonferroni Hochberg GT2 Tukey HSD Tukey Gabriel Hochberg GT2 Gabriel Dunnett t 1 ( ) < > Ryan Einot Gabriel Welsch (R-E-G-W) 2 R-E-G-W F F R-E-G-W Q Duncan Student-Newman-Keuls ( ) Tamhane T2 (t ) Dunnett T3 ( ) Games-Howell ( ) Dunnett C () Duncan Student-Newman-Keuls (S-N-K) Tukey b Waller-Duncan t Bayesian Scheffé Scheffé (LSD) t : LSD Sidak Bonferroni Games-Howell Tamhane T2 T3 Dunnett C Dunnett T3 11 GLM 1 49
56 S-N-K Tukey b Duncan R-E-G-W F R-E-G-W Q Waller Tukey HSD Hochberg GT2 Gabriel Scheffé GLM : : : (LSD) Bonferroni Sidak : 2 2 t L Leene : UNIANOVA (DESIGN ) () (TEST ) (CONTRAST ) (MISSING ) EPS (CRITERIA ) L M K (LMATRIX MMATRIX KMATRIX ) (CONTRAST ) 50 IBM SPSS Statistics Base 22
57 (CONTRAST ) (POSTHOC ) (EMMEANS ) (SAVE ) (OUTFILE ) (OUTFILE ) (OUTFILE ) GLM IBM SPSS Statistics : : WLS : : Cook : Cook D : WLS : : WLS : Pearson 0 1 : : : IBM SPSS Statistics 11 GLM 1 51
58 t GLM : : : (LSD) Bonferroni Sidak : 2 2 t L Leene : UNIANOVA (DESIGN ) () (TEST ) (CONTRAST ) (MISSING ) EPS (CRITERIA ) L M K (LMATRIX MMATRIX KMATRIX ) 52 IBM SPSS Statistics Base 22
59 (CONTRAST ) (CONTRAST ) (POSTHOC ) (EMMEANS ) (SAVE ) (OUTFILE ) (OUTFILE ) (OUTFILE ) 11 GLM 1 53
60 54 IBM SPSS Statistics Base 22
61 Pearson Spearman Kendall b () Pearson 2 Pearson : NBA Pearson (0.581) 0.01 ( 0.401) 0.05 : : : Pearson Spearman Kendall b 2 : Pearson Spearman Kendall b : Pearson 2 2 > > : Pearson Kendall b Spearman -1 ( ) +1 ( ) 0 : : 5% 1 1 % 2 2 : Pearson Copyright IBM Corp. 1989,
62 : : Pearson 2 N 1 : : : 1 CORRELATIONS NONPAR CORR Pearson (MATRIX ) 2 (VARIABLES WITH ) 56 IBM SPSS Statistics Base 22
63 : : : 0 : : 2 1. > > : : 5% 1 1% 2 0 : 0 : : : : 0 Copyright IBM Corp. 1989,
64 0 PARTIAL CORR 0 (MATRIX ) 2 (VARIABLES WITH ) ( VARIABLES ) (VARIABLES ) (FORMAT ) (STATISTICS ) 58 IBM SPSS Statistics Base 22
65 14 () : MPG ( 1 ) : () ChebycheMinkowski Lance Williams Pearson 2 Russel Rao Jaccard Dice Rogers Tanimoto Sokal Sneath 1 Sokal Sneath 2 Sokal Sneath 3 Kulczynski 1 Kulczynski 2 Sokal Sneath 4 Hamann Anderberg D Yule Y Yule QSokal Sneath > > (2 ) 1 : ChebycheMinkowski : : Lance Williams ( 2 ) 2 z Copyright IBM Corp. 1989,
66 0 1 ( 2 ) 1 : Pearson 2 : Russell RaoJaccard Dice Rogers Tanimoto Sokal Sneath 1 Sokal Sneath 2 Sokal Sneath 3 Kulczynski 1 Kulczynski 2 Sokal Sneath 4 Hamann Anderberg D Yule Y Yule QSokal Sneath 5 4 ( 2 ) 2 z PROXIMITIES PROXIMITIES Minkowski 60 IBM SPSS Statistics Base 22
67 15 1 ( ) : : 1 1 () () ( ) : 1000 Statistics Base > > ?: : ( ): Copyright IBM Corp. 1989,
68 (): () () (IBM SPSS Statistics Serer ): IBM SPSS Statistics Serer 64 : : ( ) (00:00:00) : 5 10 : ( 3) : : (p 0.1 ) 1 62 IBM SPSS Statistics Base 22
69 : : () : : (AICC) F R2 (ASE) ( (ASE)) 30% F F P p 0.05 P p 0.10 : : 3 : (AICC) R2 (ASE) ( (ASE)) 30% : 15 63
70 : : : : : PredictedValue :.zip : : 100 R 2 64 IBM SPSS Statistics Base 22
71 (ADP) ADP () : ( ) : ( ) 5 10 ( 3) (p 0.05 ) / / 1 ADP : 15 65
72 : P-P : - S ID ( ) Cook Cook Cook : : (p ) p : : 10 : 1.00 () ( ) 1 : 66 IBM SPSS Statistics Base 22
73 : ( ) (p ) p : T () : 10 : 1.00 : : 10 :
74 ( 1 ) 68 IBM SPSS Statistics Base 22
75 16 1 () : 1 : : : R R 2 R 2 R 2 95% Durbin-Watson (Mahalanobis Cook) DfBeta DfFit : : 2 ( ) : 1. > > WLS WLS. 0 Copyright IBM Corp. 1989,
76 Enter (Regression) ( ()). 1 1 Stepwise (). F F Remoe ( ). 1 1 Backward Elimination ( ). Forward Selection ( ). 1 1 ( ) IBM SPSS Statistics Base 22
77 : 2 : (DEPENDNT) ( (*ZPRED) (*ZRESID) (*DRESID) (*ADJPRED) (*SRESID) (*SDRESID)) : 2 : (*ZPRED *ZRESID) : 1 : : 0 1 : : : Mahalanobis: 1 Cook: Cook D : 1 0 ( ) (N-1)/N : Mean (). (2 ) : 1 (2 ) :
78 : : Pearson 0 1 : : : : (DfBeta) (DfFit) DfBeta DfFit DfBeta. () Standardized DfBeta ( DfBeta). 2/(N ) (N ) () DfFit. Standardized DfFit ( DfFit). p/n 2 p N : 1 : XML ( ) XML (PMML) : B B B t t : R R 2 R 2 72 IBM SPSS Statistics Base 22
79 R2 : R 2 R 2 : Partial Correlation ( ). 2 2 Part Correlation ( ( )). R2 : () 1 (VIF) : Durbin-Watson ( n ) : F F Use Probability of F (F ). F Use F Value (F ). F : R 2 : : : N : 16 73
80 REGRESSION (MATRIX ) (CRITERIA ) (METHOD DEPENDENT ) (DESCRIPTIVES STATISTICS ) 74 IBM SPSS Statistics Base 22
81 17 McCullagh ( ) PLUM () () cm 140 cm 10 cm 210 cm 200 cm : : Pearson Pearson 2 2 Cox Snell R 2 Negalkerke R 2 McFadden R 2 : : 1 : 1. > > OK Copyright IBM Corp. 1989,
82 : : 0 2 : : 0 : 0 : : 5 : f(x)=log(x/(1 x) ) : f(x)=log( log(1 x)) : f(x)= log( log(x)) : f(x)=φ 1 (x) (): f(x)=tan(π(x 0.5)) : : : Pearson 2 2 : Cox Snell R 2 Nagelkerke R 2 McFadden R 2 : : : 76 IBM SPSS Statistics Base 22
83 : Pearson ( ) : : / / : / / / : : / : : : 2 : 2 3 : 3 4 : 4 5 :
84 / : : : 2 : 2 3 : 3 4 : 4 5 : 5 PLUM PLUM 78 IBM SPSS Statistics Base 22
85 18 11 : E E 2 3 : : R R 2 R 2 : 2 3 S : ( ) ( ) : ( ) 1. > > ( ) 4. 1 : : : Copyright IBM Corp. 1989,
86 1 : Y=b0+(b1*t) : Y = b0 + (b1 * ln(t)) : Y=b0+(b1/t) 2 : Y = b0 + (b1 * t) + (b2 * t**2) 2 3 : Y = b0 + (b1 * t) + (b2 * t**2) + (b3 * t**3) : Y = b0 * (t**b1) ln(y) = ln(b0) + (b1 * ln(t)) : Y = b0 * (b1**t) ln(y) = ln(b0) + (ln(b1) * t) S : Y = e**(b0 + (b1/t)) ln(y) = b0 + (b1/t) : Y=1/(1/u + (b0 * (b1**t))) ln(1/y-1/u) = ln (b0) + (ln(b1) * t) u : Y = e**(b0 + (b1 * t)) ln(y) = b0 + (b1 * t) : Y = b0 * (e**(b1 * t)) ln(y) = ln(b0) + (b1 * t) : ( ) ( ) : : : ( ) 80 IBM SPSS Statistics Base 22
87 19 (PLS (projection to latent structure) ) PLS (OLS) PLS PLS : ( ) (VIP) ( ) : (VIP) 3 : () ( ) : one-of-c c c (1,0,...,0) (0,1,0,...,0) (0,0,...,0,1) : 0.5 : : > > Copyright IBM Corp. 1989,
88 ( ) ID 2 Python IBM SPSS Statistics - Essentials for Python IBM SPSS Statistics NumPy SciPy Python : (IBM SPSS Statistics Serer ) NumPy SciPy Windows Mac Windows Mac IBM SPSS Statistics Python 2.7 NumPy SciPy Python Python 2.7 NumPy SciPy NumPy SciPy NumPy SciPy Python 2.7 Python Python (> ) Linux NumPy SciPy NumPy SciPy IBM SPSS Statistics Python 2.7 IBM SPSS Statistics Python NumPy SciPy IBM SPSS Statistics Python 2.7 Python Python (> ) Windows Unix NumPy SciPy IBM SPSS Statistics Python 2.7 Python Python 2.7 NumPy SciPy NumPy SciPy Python NumPy SciPy Python 2.7 Python IBM SPSS Statistics Administration Console 82 IBM SPSS Statistics Base 22
89 : : : : : 2 : 2 3 : 3 4 : 4 5 : 5 : : : (VIP) VIP 19 83
90 84 IBM SPSS Statistics Base 22
91 20 2 ( ) ( ) k : Nominal ( ). ( ) Ordinal ( ). ( ) Scale ( ). ( ) 1. () n/a 85
92 : one-of-c c c (1,0,...,0) (0,1,0,...,0) (0,0,...,0,1) one-of-c (88 ) : (88 ) : : Mersenne Twister (88 ) > > ( ): ( ) k : [2*(x min)/(max min)] ( ): ( ) k 86 IBM SPSS Statistics Base 22
93 ( ): 1 ( ) : : (k): k ( ) k V : : x y 2 : 2 Manhattan
94 : : ( ) : : 92 : : : 0 : V 88 IBM SPSS Statistics Base 22
95 2 2 3 : 1 V ( ) : 1 V () Mersenne Twister : Mersenne Twister : : : n n /: () : : : k
96 XML : k : ( ) k k : ( ) ( 4 ) : () / k 90 IBM SPSS Statistics Base 22
97 : Ctrl (k) ( ) / // 1. > > > 4. x y z 20 91
98 X 1.0 k : 2 () k k k i i i i i k ( ) y x y ( ) x (x ) 92 IBM SPSS Statistics Base 22
99 k y ( ) x (k) k k (92 ) k k ( ) : (100% ) 20 93
100 94 IBM SPSS Statistics Base 22
101 21 1 () : 3 : a b c d D=a*climate +b*urban +c*population +d*gross domestic product per capita 2 D 4 : : 1 : Box M : Wilks 2 : Fisher Wilks : : ( ) ( ) ( IQ IQ ) 1. > > Copyright IBM Corp. 1989,
102 3. () 4. : : 5. : : : : ( ) 1 Box M Means ( ( )). Uniariate ANOVAs (1 ()). Box M : p : Fisher Fisher's (Fisher ). Fisher () Unstandardized ( ()). : : : 96 IBM SPSS Statistics Base 22
103 : : 1 : : Wilks Mahalanobis F Rao V Rao V V Wilks : 1 Wilks Wilks : Mahalanobis : 1 F : F Rao V: Lawley-Hotelling Rao V F F F : F F : F : Mahalanobis F F : : : : 50% 25% 2 25%
104 : Leae-one-out : : Leae-one-out : U : : : : () 2 : : 2 1 : 2 1 : 1 : () ( 1 ) ( 1 ) XML DISCRIMINANT (1 ) (ANALYSIS ) (PRIORS ) (ROTATE ) (FUNCTIONS ) ( ) (SELECT ) (MATRIX ) 98 IBM SPSS Statistics Base 22
105 (MATRIX ) 21 99
106 100 IBM SPSS Statistics Base 22
107 22 () ( ) : : : : ( ) Kaiser-Meyer-Olkin Bartlett : : 2 3 : () Pearson : 2 () ( ) 1. > > Copyright IBM Corp. 1989,
108 1. 2. : 1 : KMO Bartlett KMO Bartlett : Kaiser-Meyer-Olkin Bartlett ( ) : ( ) : : Principal Components Analysis ( ()). 1 Unweighted Least-Squares Method ( 2 ()). 1 ( ) Generalized Least-Squares Method ( 2 ()). 1 Maximum-Likelihood Method ( ()). 1 Principal Axis Factoring ( ()). 2 Alpha ( ()). 102 IBM SPSS Statistics Base 22
109 Image Factoring ( ()). Guttman ( ) : : : : : Unrotated Factor Solution ( ()). ( ) : () : : Varimax Method ( ()). 1 : ( ) 1 0( ) Quartimax Method ( ()). 1 Equamax Method ( ()). 1 Promax Rotation ( ()). 1 : 2 3 Rotated Solution ( ()). Factor Loading Plot ()
110 : : 1 : Bartlett Anderson-Rubin Regression Method ( ()) Bartlett : 1 0 Anderson-Rubin Method (Anderson-Rubin ()). 1 Bartlett 0 1 : : : FACTOR 104 IBM SPSS Statistics Base 22
111 23 TwoStep TwoStep : TwoStep XML TwoStep : ( ) ()2 : IBM SPSS Statistics IBM SPSS Statistics Copyright IBM Corp. 1989,
112 106 IBM SPSS Statistics Base 22
113 24 TwoStep TwoStep () : : : (CF) TwoStep : : 2 : : 2 : : : : 108 TwoStep : (BIC) (AIC) TwoStep : : Copyright IBM Corp. 1989,
114 : ( ) TwoStep 1. > > TwoStep XML TwoStep : (CF) CF CF CF CF CF CF CF 1 ID : (MB) IBM SPSS Statistics Base 22
115 : CF : (CF) : CF CF (): 1 : 1 CF : (b d+1 1)/(b 1) CF b d CF 16 : CF XML XML TwoStep CF CF : 1 TwoStep TwoStep : : : : 24 TwoStep 109
116 : : ID tsc_n n XML : XML CF 2 : XML (PMML) CF : () 2 1 ( ) 2 2 ( ) / IBM SPSS Statistics Base 22
117 ( ) () () Kaufman Rousseeuw (1990) Kaufman Rousseeuw (B A) / max(a,b) A B : (TwoStep ) : : : : ( ) ( ) : ( ) (55 $100,000 ) : : 24 TwoStep 111
118 / : 4.32 () : : : : : : : : // () 112 IBM SPSS Statistics Base 22
119 : / : / : () () Ctrl Shift : 5 () 24 TwoStep 113
120 4 4 () : : ( ) : 114 IBM SPSS Statistics Base 22
121 : : : Ctrl 2. > OK 24 TwoStep 115
122 116 IBM SPSS Statistics Base 22
123 25 ( ) ( ) 1 :? ( ) ( ) : () : : 2 ( ) ( ) ( ) : : ( ) 1. > > : Ward 117
124 : : Pearson Chebyche Minkowski : : 4 Anderberg D Dice Hamann Jaccard Kulczynski 1 Kulczynski 2 Lance-Williams Ochiai Rogers-Tanimoto Russel-Rao Sokal-Sneath 1 Sokal-Sneath 2 Sokal-Sneath 3 Sokal-Sneath 4 Sokal-Sneath 5 Yule Y Yule Q : (2 ) z : 0 1 : ( ) : : 1 : : : CLUSTER CLUSTER IBM SPSS Statistics Base 22
125 ()
126 120 IBM SPSS Statistics Base 22
127 26 F ( ) : ( ) k ( ) : : : : 2 : : () ( ) 1. Copyright IBM Corp. 1989,
128 > > ( 2 ) : Quick Cluster 1 : % : : IBM SPSS Statistics Base 22
129 : : : 1 ANOVA table ( (/ )). 1 F F 1 : : : : QUICK CLUSTER QUICK CLUSTER k
130 124 IBM SPSS Statistics Base 22
131 ?: : Kolmogoro-Smirno : : 1 > > : 1 : Copyright IBM Corp. 1989,
132 : 1 : ( ) Kolmogoro-Smirno : 2 (2 ): 2 ( 2 ) ( 2 ): (Kolmogoro-Smirno ): Kolmogoro-Smirno (Wilcoxon ): Wilcoxon 1 (): : 2 ( 2 ) : (p) : 2 Clopper-Pearson (): 2 Jeffreys: Jeffreys p : p 126 IBM SPSS Statistics Base 22
133 :( ( )) :( ( )) 2 : : : /6 1/3 2 1/2 3 Kolmogoro-Smirno : : : : : : ( 2 ) :
134 : : () (%): : : NPTESTS ?: : 2 Mann-Whitney U k Kruskal-Wallis 128 IBM SPSS Statistics Base 22
135 : : > > : 1 : : 1 : : 2 Mann-Whitney U k Kruskal-Wallis : :
136 Mann-Whitney U(2) Kolmogoro-Smirno (2 ) 2 2 (2 Wald-Wolfowitz) 2 Kruskal-Wallis ANOVA (k ) Mann-Whitney U k (k Jonckheere-Terpstra) k Kruskal-Wallis k k Jonckheere-Terpstra k : Moses (2 ) 2 2 : (k ) ( ) k : Hodges-Lehman (2 )2 2 : () (%): IBM SPSS Statistics Base 22
137 : : NPTESTS : 2?: : 2 McNemar 3 Cochran Q 2 Wilcoxon 3 Friedman : 3 2 Friedman McNemar > >
138 : 2 : : 2 : 2 McNemar 3 Cochran Q 2 Wilcoxon 3 Friedman : 2 : McNemar (2 ) 2 ( 2 ) McNemar : Cochran Q(k) k ( 2 ) k 133 Cochran Q: : (2 ) 2 McNemar IBM SPSS Statistics Base 22
139 : : 2 3 : Kendall (k ) ( ) 1 k : Friedman (k k k McNemar : : McNemar ( 2 ) : 2 Cochran Q: : Cochran Q ( 2 ) : 2 : ()
140 (%): : : NPTESTS ( ) / IBM SPSS Statistics Base 22
141 p () Wilcoxon
142 Wilcoxon Kolmogoro-Smirno Kolmogoro-Smirno McNemar McNemar 2 2 Wilcoxon Wilcoxon 136 IBM SPSS Statistics Base 22
143 Cochran Q Cochran Q Friedman Friedman Kendall Kendall Mann-Whitney Mann-Whitney Kolmogoro-Smirno Kolmogoro-Smirno
144 Wald-Wolfowitz Wald-Wolfowitz Kruskal-Wallis Kruskal-Wallis Jonckheere-Terpstra Jonckheere-Terpstra Moses Moses ID k 138 IBM SPSS Statistics Base 22
145 k ( ) 1 NPTESTS : 2 2 : 2 2 : Kolmogoro-Smirno : ( ) 2 : 1 2 Mann-Whitney U 2 Kolmogoro-Smirno Moses Wald-Wolfowitz 2 : 2Wilcoxon McNemar : 1 2 Kruskal-Wallis Jonckheere-Terpstra
146 : 2Friedman Kendall W Cochran Q : 2 5% 30% 10% 20% 15% 15% : : ( ) : % 2 1. > > > : : /16 4/16 5/16 4/ IBM SPSS Statistics Base 22
147 2 : : 4 : : : : NPAR TESTS ( 2 ) (CHISQUARE ) 1 (EXPECTED ) : ( ) 2 3/4 (0.0027) 1/2 : 4 2 : 2 2 true false : 2 1. > > >
148 : : 4 : : : : NPAR TESTS (2 ) 3 (BINOMIAL ) (BINOMIAL ) (EXPECTED ) 2 : : 4 : : 1. > > > IBM SPSS Statistics Base 22
149 : 2 1 : : 4 : : : : NPAR TESTS () (RUNS ) 1 (RUNS ) 1 Kolmogoro-Smirno 1 Kolmogoro-Smirno 1 () Kolmogoro-Smirno Z ( ) : 1 Kolmogoro-Smirno : 4 1 Kolmogoro-Smirno : ( ) : Kolmogoro-Smirno K-S Lilliefors ( ) 1 Kolmogoro-Smirno
150 > > > 1 K-S Kolmogoro-Smirno : : 4 : : : : NPAR TESTS (1 Kolmogoro-Smirno ) (K-S ) : Mann-Whitney U : 4 : Mann-Whitney U Moses Kolmogoro-Smirno Z Wald-Wolfowitz 2 : : Mann-Whitney U > > > IBM SPSS Statistics Base 22
151 : 2 () 4 Mann-Whitney U 2 2 Wilcoxon Kruskal-Wallis Mann-Whitney Mann-Whitney U 2 Wilcoxon W W 2 : Kolmogoro-Smirno Z Wald-Wolfowitz Kolmogoro-Smirno 2 Wald-Wolfowitz 2 2 Moses 2 : 1 1 5% : : 4 : : : :
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