There are two types of problems that occurs within a process: problems with variation and problems with centering. The goal is to have a process which is centered on a target with minimum variation. Continuous decision tools are used to describe the nature and extent of these two types of problems when studying a continuous CT characteristic.This With variation problems, we make inferences about the standard deviation or the variance of the population based upon sample data. With centering problems, we make inferences about the mean or the median of the population based upon sample data.

Confidence Intervals allow us to estimate population parameters (mean, standard deviation, etc.) within a range of values set at a pre-assigned probability called “confidence level”.

The One Sample t-Test and Z-Test are used to compare the population mean with a value such as the target. The t-Test is normally used for small sample size ( 30). However, as the sample size increases, the results of the t-Test approach the Z -Test.

When we want to determine the influence of factors such as procedures, material type, assembly sequence, temperature, etc. on our process, we can use the Two- Sample t-Test to determine if the mean of a CT characteristic significantly changes under two different conditions. For 2 or more conditions, we can use the One-Way ANOVA Test (analysis of variance).

The homogeneity of variance tests is used to determine if the variances of a CT characteristic significantly changess under two or more different conditions introduced by one factor.

The Two-Way ANOVA allows us to analyse the effect of two factors on a CT characteristic. An interaction effect between the two factors means that they have a combined effect on the CT characteristic.

The correlation coefficient is used to calculate the strength of the linear relationship between a continuous dependent variable (Y) and a continuous independent variable (X). When the relationship is strong, an equation that describes this relationship can be obtained through linear regression analysis. A fitted line plot allows us to visualize the equation with prediction bands. Most of the continuous decision tools assume that the data comes from a normal population. When it is impossible to characterize the population distribution, or when the distribution is not normal, nonparametric tests can be used. With these tests, instead of studying the mean, the median is used because it is a more appropriate measure of central tendency for data coming from non-symmetrical distributions.