Gallery of Quantitative Techniques from the Handbook
Univariate y = c + e	Estimation Measures of location Measures of variation Measures of skewness Confidence interval for the mean 1-Sample t-test Tolerance intervals Testing proportion of defectives Chi-Square test for the standard deviation Testing the process/material defect density Test for randomness Autocorrelation Runs test Test for outliers Grubbs test Test for goodness of fit Shapiro-Wilks test Anderson-Darling test Chi-Square test Kolmogorov-Smirnov test Sample size computations Sample size for mean Sample size for standard deviation Sample size for proportion Sample size for defect densities

Time Series y = f(t) + e	Exponential Smoothing ARIMA (Box-Jenkins) Modeling

1 Factor y = f(x) + e	2-Sample t-test for equal means Mann-Whitney test for equal means Testing whether 2 processes produce same proportion of defectives F test for equal variances Bartlett test for homogeneous variances Levene test for homogeneous variances One way analysis of variance Kruskal-Wallis non-parameteric analysis of variance

Multi-Factor y = f(xp, x1,x2,...,xk) + e	Two way analysis of variance Chi-square test for independence

Regression y = f(x1,x2,x3,...,xk) + e	Linear least sum of squares regression Nonlinear least sum of squares regression Weighted least sum of squares regression Locally weighted least squares (LOWESS)