Implement data analysis and multivariate statistical analysis. 1. Probability analysis – compute and graph probability density function values, cumulative distribution function values, survival function values, hazard function values, quantile values, means and variances. 2. Compute descriptive statistics of selected data. 3. Frequency analysis. 4. Compare means – one sample t test, independent-samples t test and paired-samples t test. 5. Compare variances – one sample and two samples. 6. Variance analysis – one-way ANOVA and two-way ANOVA. 7. Z test. 8. Correlation tests. 9. Jarque Bera test. 10. Non-parametric tests, including one sample Chi-square test, two samples Chi-square test, one sample K-S test, two samples K-S test, sign test, Wilcoxon signed rank test, Mann Whitney U test. 11. Regression analysis, including univariate linear regression, multivariate linear regression, linear curve fitting, nonlinear curve fitting, trend surface analysis, stepwise regression etc. 12. Correlation analysis, including bivariate correlation analysis, partial correlation analysis and canonical correlation analysis. 13. Cluster analysis, including stepwise cluster analysis, hierarchial cluster analysis and dynamical cluster analysis. 14. Stepwise discriminant analysis. 15. Principal component analysis. 16. Factor analysis. 17. Correspondence analysis. 18. Statistics plots. 19. Data visualization.

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