Applied Statistics Using SPSS, STATISTICA, MATLAB and R

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8.3 Principal Components of Correlation Matrices 343 variables as well as the data groups that best reflect the behaviour of those variables. Figure 8.7. Dimensionality reduction of the first two classes of cork-stoppers: a) Eigenvalues; b) Principal component scatter plot (compare with Figure 6.5). (Both graphs obtained with STATISTICA.) Consider the means of variable F1 in Example 8.6: 0.71 for class 1 and −0.71 for class 2 (see Figure 8.7b). As expected, given the translation y = x – x , the means are symmetrically located around F1 = 0. Moreover, by visual inspection, we see that the class 1 cases cluster on a high F1 region and class 2 cases cluster on a low F1 region. Notice that since the scatter plot 8.7b uses the projections of the standardised data onto the F1-F2 plane, the cases tend to cluster around the (1, 1) and (−1, −1) points in this plane. Figure 8.8. Factor loadings table (a) with significant correlations in bold and graph (b) for the first two classes of cork-stoppers, obtained with STATISTICA.
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Applied Statistics Using SPSS, STAT
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E d itors Prof. Dr. Joaquim P. Marq
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Contents Preface to the Second Edit
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Contents ix 5.2.3 The Chi-Square Te
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Contents xi Appendix A - Short Surv
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Contents xiii E.26 Soil Pollution .
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Preface to the First Edition This b
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Symbols and Abbreviations Sample Se
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|A| determinant of matrix A tr(A) t
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Σ covariance matrix x arithmetic m
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1 Introduction 1.1 Deterministic Da
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18 h 16 14 12 10 8 6 4 2 0 1.1 Dete
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1.2 Population, Sample and Statisti
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Table 1.3 1.2 Population, Sample an
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Table 1.4 1.3 Random Variables 9 Da
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1.4 Probabilities and Distributions
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1.5 Beyond a Reasonable Doubt... 13
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1.5 Beyond a Reasonable Doubt... 15
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1.6 Statistical Significance and Ot
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1.8 Software Tools 19 book we will
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1.8 Software Tools 21 In the follow
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1.8 Software Tools 23 illustrates t
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1.8 Software Tools 25 On-line help
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1.8 Software Tools 27 Figure 1.12.
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2 Presenting and Summarising the Da
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2.1 Preliminaries 31 The data can t
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» meteo=[ 181 143 36 39 37 % Pasti
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2.1 Preliminaries 35 are interested
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2.1 Preliminaries 37 Besides the in
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2.2 Presenting the Data 39 Sorting
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2.2 Presenting the Data 41 In Table
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2.2 Presenting the Data 43 With SPS
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2.2 Presenting the Data 45 Figure 2
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2.2 Presenting the Data 47 Figure 2
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2.2 Presenting the Data 49 Let X de
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2.2 Presenting the Data 51 Commands
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2.2 Presenting the Data 53 A: The c
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2.2 Presenting the Data 55 The s, c
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2.2 Presenting the Data 57 histogra
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2.3 Summarising the Data 59 type da
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2.3 Summarising the Data 61 delimit
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2.3 Summarising the Data 63 The sam
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Note that: 2.3 Summarising the Data
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where sXY, the sample covariance of
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2.3 Summarising the Data 69 STATIST
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2.3 Summarising the Data 71 A: The
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2.3.6 Measures of Association for N
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2.3 Summarising the Data 75 with th
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Exercises 77 A: We use the N, S and
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Exercises 79 2.13 Determine the box
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3 Estimating Data Parameters Making
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3.1 Point Estimation and Interval E
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3.2 Estimating a Mean 85 In Chapter
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3.2 Estimating a Mean 87 There are
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3.2 Estimating a Mean 89 A: Using M
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3.2 Estimating a Mean 91 Figure 3.5
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3.3 Estimating a Proportion 93 esti
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3.4 Estimating a Variance 95 is to
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3.5 Estimating a Variance Ratio 97
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3.6 Bootstrap Estimation 99 i. F df
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3.6 Bootstrap Estimation 101 about
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3.6 Bootstrap Estimation 103 The bi
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3.6 Bootstrap Estimation 105 In the
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Exercises 107 In order to obtain bo
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Exercises 109 3.14 Consider the CTG
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112 4 Parametric Tests of Hypothese
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5 Non-Parametric Tests of Hypothese
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5.1 Inference on One Population 173
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s = npq = 224× 0. 75× 0. 25 = 6.4
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5.1.3 The Chi-Square Goodness of Fi
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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 F
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5.1.5 The Lilliefors Test for Norma
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5.2 Contingency Tables 189 fewer mi
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2 1 5.2 Contingency Tables 191 degr
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5.2 Contingency Tables 193 An alter
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5.2 Contingency Tables 195 male and
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5.2 Contingency Tables 197 first ca
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5.2 Contingency Tables 199 very low
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5.3.1 Tests for Two Independent Sam
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5.3 Inference on Two Populations 20
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Example 5.19 5.3 Inference on Two P
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3 ( N − N ) 5.4 Inference on More
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5.4 Inference on More Than Two Popu
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5.4 Inference on More Than Two Popu
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Exercises 219 5.7 Several previous
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Exercises 221 5.23 Run the non-para
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6 Statistical Classification Statis
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x 2 o o o o o o o o oo o o o o o o
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6.2 Linear Discriminants 227 Figure
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6.2 Linear Discriminants 229 Figure
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6.2 Linear Discriminants 231 Let us
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Table 6.5. Summary of minimum dista
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6.3 Bayesian Classification 235 Not
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6.3 Bayesian Classification 237 Fig
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6.3 Bayesian Classification 239 Let
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6.3 Bayesian Classification 241 Not
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0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15
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6.3 Bayesian Classification 245 For
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Reality A N Decision A N a b c d 6.
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6.4 The ROC Curve 249 Figure 6.17.
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6.4 The ROC Curve 251 In order to o
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6.5 Feature Selection 253 A: The RO
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6.5 Feature Selection 255 comfortab
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6.6 Classifier Evaluation 257 Resub
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259 A: Table 6.12 shows the leave-o
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6.7 Tree Classifiers 261 Figure 6.2
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6.7 Tree Classifiers 263 or not a g
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6.7 Tree Classifiers 265 The classi
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6.7 Tree Classifiers 267 The classi
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Exercises 269 6.2 Repeat the previo
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7 Data Regression An important obje
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7.1.2 Estimating the Regression Fun
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7.1 Simple Linear Regression 275 Fi
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2 2 2 7.1 Simple Linear Regression
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7.1 Simple Linear Regression 279 Co
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Example 7.3 7.1 Simple Linear Regre
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7.1 Simple Linear Regression 283 Th
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7.1 Simple Linear Regression 285 Fi
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7.1 Simple Linear Regression 287 Le
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7.2 Multiple Regression 7.2.1 Gener
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7.2 Multiple Regression 291 For the
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YYˆ = ∑ ∑ ( y ( y − y)( yˆ
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7.2 Multiple Regression 295 Figure
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7.2 Multiple Regression 297 Table 7
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7.2.5.1 Tests for Regression Coeffi
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The Yi can also be linearly modelle
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3000 2000 1000 0 -1000 -2000 Raw Re
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7.3 Building and Evaluating the Reg
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There are other ways to detect outl
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a 4 3 2 1 0 -1 -2 -3 Expected Norma
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Example 7.19 7.4 Regression Through
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Page 724: 344 8 Data Structure Analysis In or
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Page 744: 354 9 Survival Analysis P( t ≤ T
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368 9 Survival Analysis The exponen
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370 9 Survival Analysis γ This is
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372 9 Survival Analysis the proport
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374 9 Survival Analysis 9.5 Compute
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376 10 Directional Data Example 10.
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380 10 Directional Data The MATLAB
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382 10 Directional Data A: We use t
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384 10 Directional Data For p = 2,
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386 10 Directional Data Thus, the r
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388 10 Directional Data from a unif
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390 10 Directional Data * 2 z = ( 1
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392 10 Directional Data 10.4.3 The
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396 10 Directional Data 10.5.2 Mean
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398 10 Directional Data Similar res
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400 10 Directional Data Exercises 1
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Appendix A - Short Survey on Probab
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A.1 Basic Notions 405 corresponding
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A.2 Conditional Probability and Ind
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A. 4 Bayes ’ Theorem 409 The firs
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A.5 Random Variables and Distributi
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a 0.5 0.4 0.3 0.2 0.1 0 f (x ) a a+
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Example A. 12 A.6 Expectation, Vari
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n [ X ] = ∑ i= 1 A.6 Expectation,
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A.7 The Binomial and Normal Distrib
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A.7 The Binomial and Normal Distrib
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The following results are worth men
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A.8.2 Moments A.8 Multivariate Dist
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For the d-variate case, this genera
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0.25 0.2 0.15 0.1 0.05 p(x) A.8 Mul
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432 Appendix B - Distributions A: T
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436 Appendix B - Distributions For
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440 Appendix B - Distributions Dist
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442 Appendix B - Distributions 0.45
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444 Appendix B - Distributions B.2.
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448 Appendix B - Distributions B.2.
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450 Appendix B - Distributions Dist
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452 Appendix B - Distributions 1 0.
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456 Appendix C - Point Estimation T
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458 Appendix C - Point Estimation
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460 Appendix D - Tables p n k 0.05
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466 Appendix D - Tables D.3 Student
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468 Appendix D - Tables D.5 Critica
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470 Appendix E - Datasets The varia
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472 Appendix E - Datasets E.6 CTG T
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474 Appendix E - Datasets E.9 FHR T
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476 Appendix E - Datasets E.14 Fore
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478 Appendix E - Datasets DATE_REOP
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480 Appendix E - Datasets CG: Conic
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482 Appendix E - Datasets E.26 Soil
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484 Appendix E - Datasets E.29 VCG
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Appendix F - Tools F.1 MATLAB Funct
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Appendix F - Tools 489 r
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References Chapters 1 and 2 Anderso
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References 493 Gardner MJ, Altman D
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References 495 Raudys S, Pikelis V
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References 497 Mardia KV, Jupp PE (
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500 Index 5.9 (two paired samples t
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502 Index H hazard function, 353 ha
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504 Index S sample, 5 mean, 416 siz
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Applied Statistics Using SPSS, STATISTICA, MATLAB and R

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