Applied Statistics Using SPSS, STATISTICA, MATLAB and R

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2.2 Presenting the Data 55 The s, c arguments of MATLAB scatter and scatter3 are the size and colour of the marks, respectively. The plot.default function is the x-y scatter plot function of R and has several configuration parameters available (colours, type of marks, etc.). The R Graphics package has no 3D plot available. Figure 2.20. Scatter plot (obtained with STATISTICA) of the variables ART and PRT of the cork stopper dataset. Figure 2.21. 3D plot (obtained with STATISTICA) of the variables ART, PRT and N of the cork stopper dataset. The most popular graphical tools for multivariate data are the scatter plots for bivariate data and the 3D plots for trivariate data. Examples of these plots, for the cork stopper data, are shown in Figures 2.20 and 2.21. As a matter of fact, the 3D
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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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Page 102: 2.1 Preliminaries 31 The data can t
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Page 118: 2.2 Presenting the Data 39 Sorting
Page 122: 2.2 Presenting the Data 41 In Table
Page 126: 2.2 Presenting the Data 43 With SPS
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Page 138: 2.2 Presenting the Data 49 Let X de
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80 2 Presenting and Summarising the
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82 3 Estimating Data Parameters los
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84 3 Estimating Data Parameters w
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86 3 Estimating Data Parameters int
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88 3 Estimating Data Parameters app
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90 3 Estimating Data Parameters m =
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92 3 Estimating Data Parameters In
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94 3 Estimating Data Parameters The
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96 3 Estimating Data Parameters est
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98 3 Estimating Data Parameters Exa
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100 3 Estimating Data Parameters Th
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102 3 Estimating Data Parameters A:
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104 3 Estimating Data Parameters A:
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106 3 Estimating Data Parameters We
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108 3 Estimating Data Parameters 3.
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4 Parametric Tests of Hypotheses In
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4.1 Hypothesis Test Procedure 113 V
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p B X 4.2 Test Errors and Test Powe
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xα = µ B −1 . 64× σ = 1300
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4.2 Test Errors and Test Power 119
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4.3 Inference on One Population 121
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Example 4.1 4.3 Inference on One Po
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4.3 Inference on One Population 125
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4.4 Inference on Two Populations 12
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Then, the following test statistic:
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4.5 Inference on More than Two Popu
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4.5.2 One-Way ANOVA 4.5.2.1 Test Pr
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145 The ANOVA test uses precisely t
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4.5 Inference on More than Two Popu
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149 transformation. Notice how the
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4.5.2.2 Post Hoc Comparisons 151 Fr
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{CON, ADI, FAD, GLA}; {ADI, FAD, GL
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155 in the values of the sample mea
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SST = c i= 1 j= 1 = r ∑∑ c ∑
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159 Sum of the squares representing
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161 Notice that in Table 4.21 the t
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163 c. The comparison between hospi
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Power 1.00 .95 .90 .85 2-Way (2 X 3
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Exercises 167 4.5 Consider the Prog
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Exercises 169 freshmen in a program
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172 5 Non-Parametric Tests of Hypot
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224 6 Statistical Classification (c
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226 6 Statistical Classification Eq
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228 6 Statistical Classification
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230 6 Statistical Classification li
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232 6 Statistical Classification It
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234 6 Statistical Classification ve
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236 6 Statistical Classification Fi
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238 6 Statistical Classification ω
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240 6 Statistical Classification ω
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242 6 Statistical Classification th
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244 6 Statistical Classification Fi
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246 6 Statistical Classification Bo
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248 6 Statistical Classification We
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250 6 Statistical Classification Th
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252 6 Statistical Classification si
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254 6 Statistical Classification In
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256 6 Statistical Classification St
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258 6 Statistical Classification St
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260 6 Statistical Classification At
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262 6 Statistical Classification pe
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264 6 Statistical Classification i(
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266 6 Statistical Classification ap
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268 6 Statistical Classification Co
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270 6 Statistical Classification 6.
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272 7 Data Regression Correlation d
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274 7 Data Regression These propert
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276 7 Data Regression The total var
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278 7 Data Regression Next, we crea
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280 7 Data Regression b 1 = ∑ ( x
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282 7 Data Regression Example 7.5 Q
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284 7 Data Regression The sampling
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286 7 Data Regression From the defi
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288 7 Data Regression * SSLF SSPE M
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290 7 Data Regression where: - y is
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292 7 Data Regression 1.0000 0.9692
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294 7 Data Regression The first lin
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296 7 Data Regression 7.2.5 ANOVA a
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298 7 Data Regression must ask whic
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300 7 Data Regression model. Simila
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302 7 Data Regression The MATLAB po
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304 7 Data Regression the same way
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306 7 Data Regression 7.3.2 Evaluat
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308 7 Data Regression The MATLAB re
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310 7 Data Regression with s 2 =
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312 7 Data Regression the threshold
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314 7 Data Regression 7.11, the lar
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316 7 Data Regression determination
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318 7 Data Regression smaller discr
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320 7 Data Regression Besides its u
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322 7 Data Regression VIF and Mean
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324 7 Data Regression Taking the na
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326 7 Data Regression Example 7.22
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328 7 Data Regression possible to p
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330 8 Data Structure Analysis In Fi
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332 8 Data Structure Analysis of th
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334 8 Data Structure Analysis 2 −
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336 8 Data Structure Analysis using
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338 8 Data Structure Analysis ∑
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340 8 Data Structure Analysis We se
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342 8 Data Structure Analysis p = 0
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344 8 Data Structure Analysis In or
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346 8 Data Structure Analysis A: On
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348 8 Data Structure Analysis ⎡0.
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350 8 Data Structure Analysis 1 0 -
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352 8 Data Structure Analysis 8.9 C
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354 9 Survival Analysis P( t ≤ T
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356 9 Survival Analysis Example 9.2
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358 9 Survival Analysis the Fatigue
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360 9 Survival Analysis “death”
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362 9 Survival Analysis A: The Hear
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364 9 Survival Analysis From Figure
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366 9 Survival Analysis denominator
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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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