Integrity Post Processing

Check the integrity of data.

Check Integrity of Data

Review a series of statistical measures on input variables and output responses in the Integrity post processing tab.

From the Post Processing step, click the Integrity tab.
From the Channel selector, select a category of information to display in the table.
- Health: High level summary of statistics used to easily spot inconsistent, non-changing, or missing data.
- Summary: Basic descriptive statistics that presents information on the data in groups such as quartiles or ranges.
- Distribution: Detailed descriptive statistics used to quantitatively describe the distribution of data points.
- Quality: Values typically used in Quality Engineering.

Integrity Tab Data

Each column in the Integrity tab displays a statistical indicator for output responses.

Column: Description
Avg Dev (Average Deviation): Average deviation is evaluated using:
$\frac{\sum_{i = 1}^{Ν} | x_{i} - \bar{x} |}{Ν}$
In Figure 2, the horizontal line represents the average of the values in the vector. The vertical lines represent the differences between the values of the vector and the average of the values. The average deviation is the average difference between the vector elements and the average of the vector elements. The sign of each element is not taken into consideration when calculating the deviation. The sign of each element is taken into consideration when calculating the average of the elements.
Figure 2.
CoV (Coefficient of Variation): Measure of the relative dispersion given by:
$C o V = \frac{S \tan d a r d D e v i a t i o n}{M e a n}$; The use of variation lies partly in the fact that the mean and standard deviation tend to change together in many experiments. The higher the CoV, the higher the variability. The lower the CoV, the less the variability of the data. CoV is seldom of interest where the mean is likely to be near zero.
Kurtosis: Measure of the flatness of a distribution.
LCL (Lower Control Limit): Mean - 3*standard_deviation
Maximum: The largest of all output response values.
Mean: The most probable value the output response would take.
Median: The median of a scalar is that value itself.; The median of a vector with an odd number of elements is a scalar that is the element at the center of the ordered vector (element (N+1)/2, where N is the number of elements).; The median of a vector with an even number of elements is a scalar that is the average value of the two elements closest to the center of the ordered vector (elements N/2 and (N+2)/2, where N is the number of elements).
Minimum: The smallest of all output response values.
Outliers: Outliers are data points that fall outside the whiskers of a box plot. To learn more about outliers, refer to About Box Plots.
RMS: The square root of the mean of the sum of the squares of all output response values is calculated using:
$\sqrt{\frac{\sum x_{i}^{2}}{Ν}}$
Skewness: Indicates whether the probability distribution is skewed to the right or to the left. If the skewness is zero, the probability distribution is symmetric about the mean of the distribution. If the skewness is less than zero, the probability distribution is skewed to the left of the mean of the distribution. If the skewness is greater than zero, the probability distribution is skewed to the right of the mean of the distribution.
Standard Deviation: Square root of the variance.; Commonly used in the measure of dispersion.
UCL (Upper Control Limit): Mean + 3*standard_deviation
Variance: Evaluated using:
$\frac{\sum_{i = 1}^{Ν} {(x_{i} - \bar{x})}^{2}}{Ν - 1}$