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Regression Analysis in NCSS
Regression analysis refers to a group of techniques for studying the relationships among two or more variables. NCSS makes it easy to run either a simple linear regression analysis or a complex multiple regression analysis. You can perform a regression analysis with modern graphical and numeric residual analysis. Major options include multiple regression, stepwise regression, correlation matrix, residual analysis, robust regression, all-possible regressions, response surface regression, Poisson regression, Cox regression, ridge regression, Weibull regression, and logistic regression.

Features
| > All-possible regressions > Alpha level is flexible > Beta coefficients > Binary variables are automatic > Biweight regression > Bootstrap Confidence Intervals > Categorical variables > Coefficient of variation > Condition number > Confidence limits > Cook’s D > Correlations > CovRatio > Cp Statistic > DFBetas > Dffits > Durbin Watson statistic > Eigenvalues > Eigenvectors > F-ratios > Hat diagonal > Hierarchal model search > Huber’s robust regression > Interactions automatic > LAV regression > Logistic regression > Multinomial logistic regression > Multiple regression > Normal probability plot > Normality tests of residuals > R-squared |
> Regression coefficient > Residual analysis and plots > Response surface analysis > Ridge regression > Robust regression > Rstudent > Serial correlation plot > Simple linear regression > Spearman’s rank correlation > Standard error of beta > Standardized coefficient > Stepwise regression > Sum of squares > T-test for beta=0 > Through the origin > Tolerance level > Variable selection > Variance inflation factor > Weighted regression > Partial correlation > Pearson’s correlation > Poisson regression > Power calculations > Predicted values > Prediction limits > Predicts new observations > Press statistic > P.C. regression > Proportional hazards > R-bar squared |
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