Sains & Teknologi

The cases and Interpretation Output Statical Parametricand Nonparametric

This book provides a comprehensive discussion of parametric and non-parametric statistics for research in both social sciences and natural sciences. The first part covers classical assumption tests (normality, multicollinearity, heteroscedasticity, autocorrelation, and linearity) as the foundation before conducting further statistical analyses. It then explores various correlation techniques, including product moment, Spearman-Brown, phi coefficient, partial correlation, and multiple correlation to measure relationships between variables. Subsequent chapters present mean difference tests, such as the t-test (one-sample, paired, and independent), followed by analysis of variance (ANOVA and MANOVA) and analysis of covariance (ANCOVA and MANCOVA). The book also examines linear and multiple regression, including models with dummy variables, as well as logistic regression (binary and multinomial). Advanced multivariate analyses such as factor analysis (EFA and CFA), path analysis, and the evaluation of validity and reliability of instruments are systematically explained.In the non-parametric section, the book introduces alternative methods such as the Mann-Whitney, Kruskal-Wallis, Wilcoxon, Friedman, and Signed tests, which are applicable when data do not meet parametric assumptions. Overall, the discussion combines theory, step-by-step procedures, and interpretation of results, making this book a practical and academic guide for students, researchers, and practitioners in applying statistical analysis. The cases and Interpretation Output Statical Parametric and Nonparametric

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