Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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lifespan in days diet Restricted diet Ad libitum diet Descriptives Mean 95% Confidence Interval for Mean 5% Trimmed Mean Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis Mean 95% Confidence Interval for Mean 5% Trimmed Mean Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis Display 2.3 Descriptives output for rat data. Lower Bound Upper Bound Lower Bound Upper Bound Statistic Std. Error 968.75 913.94 27.641 1023.55 988.31 1035.50 80985.696 284.580 105 1435 1330 311.50 -1.161 .235 1.021 .465 684.01 14.213 655.77 712.26 695.05 710.00 17978.579 134.084 89 963 874 121.00 -2.010 .255 7.027 .506 group). Other measures of spread, such as the standard deviation and the range of the sample, confirm the increased variability in the restricted diet group. Finally, SPSS provides measures of two aspects of the “shape” of the lifespan distributions in each dietary group, namely, skewness and kurtosis (see Everitt and Wykes, 1999). The index of skewness takes the value zero for a symmetrical distribution. A negative value indicates a negatively skewed distribution, a positive value a positively skewed distribution — Figure 2.1 shows an example of each type. The kurtosis index measures the extent to which the peak of a unimodal frequency distribution departs from the shape of normal distribution. A value of zero corresponds to a normal distribution; positive values indicate a distribution that is more pointed than a normal distribution and a negative value a flatter distribution — Figure 2.2 shows examples of each type. For our data we find that the two shape indices indicate some degree of negative skewness © 2004 by Chapman & Hall/CRC Press LLC
Figure 2.1 Examples of skewed distributions. Figure 2.2 Curves with different degrees of kurtosis. and distributions that are more pointed than a normal distribution. Such findings have possible implications for later analyses that may be carried out on the data. We can now move on to examine the graphical displays we have selected. The box plots are shown in Display 2.4. (We have edited the © 2004 by Chapman & Hall/CRC Press LLC
Page 2 and 3: A Handbook of Statistical Analyses
Page 4 and 5: Preface SPSS, standing for Statisti
Page 6 and 7: Distributors The distributor for SP
Page 8 and 9: 3 Simple Inference for Categorical
Page 10 and 11: 10 Survival Analysis: Sexual Milest
Page 12 and 13: 1. SPSS Base (Manual: SPSS Base 11.
Page 14 and 15: Display 1.1 Initial SPSS for Window
Page 16 and 17: Display 1.3 Variable View window of
Page 18 and 19: three-periods symbol and filling in
Page 20 and 21: Display 1.6 Opening an existing SPS
Page 22 and 23: Display 1.8 Typical dialogue box. s
Page 24 and 25: Transpose… opens a dialogue for s
Page 26 and 27: Display 1.11 Selecting subsets of c
Page 28 and 29: A large number of functions are sup
Page 30 and 31: Display 1.15 Graph procedures demon
Page 32 and 33: More than one Output Viewer can be
Page 34 and 35: The Chart Editor facilities are des
Page 36 and 37: Display 1.19 Syntax Editor showing
Page 38 and 39: Table 2.1 Lifespans of Rats (in Day
Page 40 and 41: The test-statistic is where - y 1 a
Page 42 and 43: For small samples, p-values for the
Page 44 and 45: Display 2.1 Data View spreadsheet f
Page 48 and 49: Lifespan in days 1600 1400 1200 100
Page 50 and 51: Display 2.6 Settings for controllin
Page 52 and 53: Display 2.8 Generating an independe
Page 54 and 55: Display 2.10 Generating a Mann-Whit
Page 56 and 57: Display 2.13 Generating descriptive
Page 58 and 59: simply because ages were measured o
Page 60 and 61: Display 2.18 Generating a Wilcoxon
Page 62 and 63: Husbands' ages (years) 80 70 60 50
Page 64 and 65: Display 2.24 Generating correlation
Page 66 and 67: Display 2.26 Fitting a simple linea
Page 68 and 69: Table 2.4 Motor Vehicle Theft in th
Page 70 and 71: 2.4.5 More on Husbands and Wives: E
Page 72 and 73: Table 3.1 Belief in the Afterlife B
Page 74 and 75: make up the table. Most commonly, a
Page 76 and 77: This is known as a hypergeometric d
Page 78 and 79: (2) Odds ratio The odds of Variabl
Page 80 and 81: Display 3.2 Cross-classifying two c
Page 82 and 83: Odds Ratio for diet (Restricted die
Page 84 and 85: Display 3.7 Data View spreadsheet f
Page 86 and 87: Display 3.10 Generating a chi-squar
Page 88 and 89: 20 10 Observed number 30 0 Psychoti
Page 90 and 91: Chi-Square Tests Value McNemar Test
Page 92 and 93: Pearson Chi-Square Likelihood Ratio
Page 94 and 95: Race of defendant found guilty of m
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Estimate ln(Estimate) Std. Error of
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treatment are independent. Estimate
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Table 4.1 Cleaning Cars Sex (1 = ma
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Table 4.2 (continued) Minimum Tempe
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A measure of the fit of the model i
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Display 4.2 Generating the partial
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We specify the dependent variable a
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for the coefficient is given by [0.
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Display 4.7 Setting inclusion and e
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Model 1 2 Model 1 2 Variables Enter
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proportion of variance of the varia
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The final graph shown in Display 4.
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Display 4.12 Generating a scatterpl
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Model 1 Model 1 Model Summary .973a
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Display 4.15 Blocking explanatory v
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1. Residual plot: This is a scatter
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8 6 4 Frequency 10 2 0 2.25 1.75 1.
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a) Latitude January minimum tempera
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Display 4.22 Extending a spreadshee
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Table 4.4 Sulfur Dioxide and Indica
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Table 4.5 Body Fat Content and Age
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Table 5.1 Fecundity of Fruit Flies
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Table 5.3 Female Social Skills Anxi
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There are some advantages (and, unf
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Display 5.1 SPSS spreadsheet contai
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Display 5.3 Defining a one-way desi
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Dependent Variable: FECUNDIT Tukey
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Click Simple in the resulting Error
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finger taps per minute Between Grou
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Display 5.13 Defining a one-way des
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5.0 4.0 Mean score 6.0 3.0 2.0 B. r
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the data to assess whether the assu
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Chapter 6 Analysis of Variance II:
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Table 6.2 Data from Slimming Clinic
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When the cells of the design have d
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Dependent Variable: reaction time p
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Display 6.5 Requesting a line chart
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Display 6.8 Confidence intervals fo
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MANUAL Total manual no manual MANUA
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In a balanced design, the type I, I
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6.4 Exercises 6.4.1 Headache Treatm
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ANCOVA assumes that there is no int
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Table 7.1 Field Independence and a
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or error term. The u i are assumed
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To convey this design to SPSS, the
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LN_FN LN_FC LN_FI LN_CN LN_CC LN_CI
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Within-Subjects Factors Measure: ME
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Measure: MEASURE_1 Within Subjects
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e used, or for a conservative appro
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Table 7.2 Visual Acuity and Lens St
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Chapter 8 Analysis of Repeated Meas
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Table 8.1 A Subset of the Data from
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Table 8.1 (continued) A Subset of t
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Box 8.1 Random Effects Models Supp
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0 10 20 30 Response Average group 1
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95% CI for mean score 30 20 10 0 N
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a) TAU group Baseline depression sc
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Display 8.6 Part of Data View sprea
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Display 8.7 Defining the variables
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variables; and the variance paramet
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Fixed Effects Random Effects Residu
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different post-treatment time point
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Iteration 0 1 2 3 4 5 6 7 8 Update
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a) Estimates of error term (residua
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Table 8.3 Depression Ratings for Su
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Constant error variance across repe
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Display 9.1 Characteristics of 21 T
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Competing models in a logistic regr
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Survived? Total Survived? Total no
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Display 9.4 Defining a five-way tab
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Display 9.6 Defining a logistic reg
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Step 1 a Step 1 PCLASS PCLASS(1) PC
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Table 9.1 Unadjusted Effects of Cat
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of all explanatory variables simult
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Step 1 a Table 9.2 LR Test Results
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log-odds of survival log-odds of su
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9.4 Exercises 9.4.1 More on the Tit
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Chapter 10 Survival Analysis: Sexua
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Table 10.1 (continued) Times to Fir
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Table 10.2 Times to Completion of t
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To compare the survivor functions b
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10.3 Analysis Using SPSS 10.3.1 Sex
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Display 10.2 Generating Kaplan-Meie
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Survival Analysis for AGESEX Age of
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Display 10.6 Requesting tests for c
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to complete the task. Children rema
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Cases available in analysis Cases d
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Display 10.10 Plotting the predicte
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Display 10.12 Saving results from a
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The smooth lines in the respective
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EFT AGE Display 10.15 Selected outp
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Table 10.3 Heroin Addicts Data Subj
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Table 10.3 (continued) Heroin Addic
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Table 11.1 Crime Rates in the U.S.
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Table 11.2 AIDS Patient’s Evaluat
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that need to be considered. If the
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the analysis is based on the correl
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We assume that the residual terms a
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Correlation Murder Rape Robbery Agg
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Murder Rape Robbery Aggravated assa
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Display 11.6 Saving factor scores.
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Display 11.8 Requesting descriptive
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a) One-factor model Goodness-of-fit
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Raw Rescaled Factor 1 2 3 4 5 6 7 8
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friendly manner doubts about abilit
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Display 11.16 Reproduced covariance
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11.4.2 More on AIDS Patients’Eval
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Table 12.1 Tibetan Skulls Data Skul
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Euclidean distances are the startin
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Fisher showed that the coefficients
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Display 12.2 Declaring a discrimina
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Box's M F Display 12.4 (continued)
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Display 12.6 (continued) Functions
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Original Cross-validated a place wh
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Display 12.10 Requesting a dendrogr
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* * * * * * H I E R A R C H I C A L
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Display 12.14 Declaring a k-means c
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12.4 Exercises Cluster Number of Ca
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Table 12.2 (continued) SIDS Data Gr
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References Agresti, A. (1996) Intro
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Greenhouse, S. W. and Geisser, S. (
Page 338:
Stevens, J. (1992) Applied Multivar
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Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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