- Page 2: Applied Statistics Using SPSS, STAT
- Page 6: E d itors Prof. Dr. Joaquim P. Marq
- Page 10: Contents Preface to the Second Edit
- Page 14: Contents ix 5.2.3 The Chi-Square Te
- Page 18: Contents xi Appendix A - Short Surv
- Page 24: Preface to the Second Edition Four
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- Page 32: xx Symbols and Abbreviations ]a, b[
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- Page 44: 2 1 Introduction In the case of the
- Page 48: 4 1 Introduction precision of the r
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16 1 Introduction Imagine then that
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18 1 Introduction population of oth
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20 1 Introduction perform menu oper
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22 1 Introduction Graphs: Operation
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24 1 Introduction >> c(1,2)={‘T80
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26 1 Introduction > x [1] 1 2 3 4 5
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28 1 Introduction Figure 1.13. A pa
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30 2 Presenting and Summarising the
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32 2 Presenting and Summarising the
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34 2 Presenting and Summarising the
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36 2 Presenting and Summarising the
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38 2 Presenting and Summarising the
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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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4.4 Inference on Two Populations 12
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4.4 Inference on Two Populations 13
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4.4 Inference on Two Populations 13
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Then, the following test statistic:
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4.4 Inference on Two Populations 13
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4.4 Inference on Two Populations 13
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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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174 5 Non-Parametric Tests of Hypot
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176 5 Non-Parametric Tests of Hypot
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178 5 Non-Parametric Tests of Hypot
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180 5 Non-Parametric Tests of Hypot
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182 5 Non-Parametric Tests of Hypot
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184 5 Non-Parametric Tests of Hypot
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186 5 Non-Parametric Tests of Hypot
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188 5 Non-Parametric Tests of Hypot
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190 5 Non-Parametric Tests of Hypot
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192 5 Non-Parametric Tests of Hypot
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194 5 Non-Parametric Tests of Hypot
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196 5 Non-Parametric Tests of Hypot
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198 5 Non-Parametric Tests of Hypot
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200 5 Non-Parametric Tests of Hypot
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202 5 Non-Parametric Tests of Hypot
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204 5 Non-Parametric Tests of Hypot
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206 5 Non-Parametric Tests of Hypot
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208 5 Non-Parametric Tests of Hypot
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210 5 Non-Parametric Tests of Hypot
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212 5 Non-Parametric Tests of Hypot
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214 5 Non-Parametric Tests of Hypot
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216 5 Non-Parametric Tests of Hypot
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218 5 Non-Parametric Tests of Hypot
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220 5 Non-Parametric Tests of Hypot
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222 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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378 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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394 10 Directional Data Example 10.
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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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434 Appendix B - Distributions A: T
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436 Appendix B - Distributions For
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438 Appendix B - Distributions A: T
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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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446 Appendix B - Distributions 1.2
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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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454 Appendix B - Distributions 0.6
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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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462 Appendix D - Tables p n k 0.05
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464 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