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)