## Using Multivariate StatisticsThis text takes a practical approach to multivariate data analysis, with an introductionto the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set - when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge ofhigher-level mathematics. *A new chapter on survival analysis (Ch. 15) allows students to analyze data where the outcome is time until something happens. This is very popular in biomedical research. *A new chapter on time series analysis (Ch. 16) encourages students to learn to model patterns in data gathered over many trials and to test for the effectiveness on an intervention ( |

### From inside the book

Results 1-3 of 27

Page 72

4.1.5 Normality, Linearity, and

procedures and most statistical tests of their outcomes is the assumption of

multivariate normality. Multivariate normality is the assumption that each variable

and all ...

4.1.5 Normality, Linearity, and

**Homoscedasticity**Underlying some multivariateprocedures and most statistical tests of their outcomes is the assumption of

multivariate normality. Multivariate normality is the assumption that each variable

and all ...

Page 79

4.1.5.3

ofVariance-Covariance Matrices For ungrouped data, the assumption of

roughly the same at ...

4.1.5.3

**Homoscedasticity**, Homogeneity of Variance, and HomogeneityofVariance-Covariance Matrices For ungrouped data, the assumption of

**homoscedasticity**is that the variability in scores for one continuous variable isroughly the same at ...

Page 119

5.3.2.4 Normality, Linearity,

screening procedures of Chapter 4 may be used to assess normality, linearity,

and

of ...

5.3.2.4 Normality, Linearity,

**Homoscedasticity**of Residuals Routine preanalysisscreening procedures of Chapter 4 may be used to assess normality, linearity,

and

**homoscedasticity**. Regression programs, however, also offer an assessmentof ...

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This book is written for students in the social sciences. The authors provide many insightful details that go beyond the theory.

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is this any good? hello

### Contents

Combining Variables | 10 |

Using the Book | 17 |

Review of Univariate and Bivariate Statistics | 31 |

Copyright | |

23 other sections not shown

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### Common terms and phrases

adjusted ARIMA assessed ATTDRUG ATTHOUSE ATTROLE autocorrelation canonical correlation canonical variates cell Chapter chi-square classification confidence intervals correlation matrix covariance matrix data set degrees of freedom deleted differences DISCRIM distribution eigenvalues equation evaluated expected frequencies factors FIT INDEX groups homoscedasticity hypothesis interaction kurtosis labeled levels linear logistic regression Mahalanobis distance main effects MANOVA mean measured missing data missing values multicollinearity multiple regression multivariate outliers normality orthogonal PACF parameter estimates partial autocorrelations plots predicted predictors procedure programs ratio READTYP regression coefficients relationship reliable researcher residuals rotation scatterplots scores Section sequential significant skewness small-sample example SPSS SPSS MANOVA standard errors statistical strength of association sum of squares survival survival analysis Syntax and Selected SYSTAT Table TIMEDRS tion transformation treatment Type I error univariate variables variance Wald test within-subjects Yes No Yes Yes Yes Yes