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 ( |
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Page 77
... scatterplots between pairs of variables . In plots where standardized residuals are plotted against predicted values , nonlinearity is indicated when most of the residuals are above the zero line on the plot at some predicted values and ...
... scatterplots between pairs of variables . In plots where standardized residuals are plotted against predicted values , nonlinearity is indicated when most of the residuals are above the zero line on the plot at some predicted values and ...
Page 119
... scatterplots provides a test of assumptions of normality , linearity , and ... scatterplots may be examined in lieu of or after initial screening runs . If ... plots directly in their regression programs . In SPSS , both predicted ...
... scatterplots provides a test of assumptions of normality , linearity , and ... scatterplots may be examined in lieu of or after initial screening runs . If ... plots directly in their regression programs . In SPSS , both predicted ...
Page 200
... PLOT permits a scatterplot of them . Figure 6.3 shows two scatterplots produced by PROC PLOT for the example using default size values for the plots . The CANCORR syntax runs a preliminary canonical correlation analysis and saves the ...
... PLOT permits a scatterplot of them . Figure 6.3 shows two scatterplots produced by PROC PLOT for the example using default size values for the plots . The CANCORR syntax runs a preliminary canonical correlation analysis and saves the ...
Contents
Using the Book | 17 |
Review of Univariate and Bivariate Statistics | 31 |
Screening Data | 56 |
Copyright | |
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addition adjusted analysis assessed associated assumption attitude canonical cell Chapter classification coding combination comparisons considered continuous correlation covariates deleted depends deviations differences discriminant function discussed distribution effects equation error estimated evaluated example expected factor Figure frequencies groups hypothesis important included indicates interaction interpretation interval labeled levels linear loadings logistic regression MANOVA matrix means measured methods missing multiple multivariate normality observed outliers output partial pattern performed plots predicted predictors probability problem procedure produce programs provides ratio regression regression coefficients relationship reliable researcher residuals rotation sample scores Selected separate shown shows significant solution Specify SPSS standard statistical step subjects sum of squares Syntax SYSTAT Table techniques tion transformation treatment Type univariate values variables variance variates women Yes Yes Yes