Statistical analysis: a computer oriented approach
Explains fundamental techniques of classical univariate and multivariate statistical analysis and usage of packaged statistical programs, progressing from background material through exploratory techniques to more complicated and specialized analyses
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accompanying tabulation analysis of variance anova model anova table approximate assume calculate called cardiac index classify CM CM CM column confidence interval continuous random variable correlation coefficient covariance matrix Data Set defined degrees of freedom denoted descriptive program diastolic differential effects equation error Example F distribution F ratio F-to-remove factor function histogram hypothesis H0 independent variables least-squares estimators linear regression Mahalanobis distance mean square mean vector measure method multiple correlation multiple correlation coefficient normal distribution null hypothesis observations obtain output packaged programs parameters percentile plot population posterior probability principal component probability procedure quantity random sample random variable regression program reject H0 residual sample mean Source of variation Step Student's t distribution subpopulation subset sum of squares systolic pressure test H0 test statistic test the hypothesis tests of hypotheses tion unbiased estimator univariate variance table