Operational Excellence through Statistical Thinking

Discover Statoscopex, our online statistical analysis software. It is aimed at Six Sigma and statistical analysis practitioners who want to be able to evaluate and fully exploit the limited decision-making potential of a data set.
Youri Buffe
Master Black Belt
Statoscopex promotes a results-driven application of Six Sigma, focused on identifying actionable effects rather than merely measuring the influence of factors.

Our approach aims to structure and systematize how impact is questioned and evaluated. Without this rigor, traditional hypothesis testing often provides limited value: it may suggest potential improvement paths, but the true impact only becomes visible after implementation. Meanwhile, more impactful opportunities may be overlooked due to the lack of upstream evaluation.

Statoscopex Study

Statoscopex performs comprehensive capability studies, estimating key statistical parameters such as mean, standard deviation, occurrence rate, and proportion.

Capability Study

Statoscopex performs full capability analysis by estimating mean, standard deviation, occurrence rate, and proportion in a single view.

Continuous Metric

Counting metric

Binary metric

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Study of The Average

Dedicated module for precise analysis, hypothesis testing, and detection of shifts or effects on the process mean.

Average estimate

Useful effect

Detection of an effect

Detection of a deviation from a target

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Study of The Standard Deviation

Tools focused on variability: study, test, and detect changes or deviations from the expected standard deviation.

Estimation of the standard deviation

Useful effect

Detection of an effect

Detection of a deviation from a target

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Study of the Rate of Occurrence

Analyzes count-based or event rates, detects deviations from a target rate, and estimates occurrence parameters with power calculation.

Estimating the rate of occurrence

Useful effect

Detection of an effect

Detection of a deviation from a target

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Study of the Proportion

Specialized analysis of binary outcomes or proportions, including deviation detection, confidence intervals, and effect size evaluation.

Estimating the proportion

Useful effect

Detection of an effect

Detection of a deviation from a target

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Advanced Features for Your Statistical Studies

Statoscopex offers more than50 analyses and 100 statistical calculations through its capability study and factor analysis functionalities, including:

For the following metrics: continuous, counting, binary.

  1. Determining the statistical accuracy of the defect rate of a process
  2. Determining the minimum sample size required to estimate the defect rate of a process

For the following parameters: mean, standard deviation, rate of occurrence, proportion.

  1. Determining the confidence interval of the parameter
  2. Determining the minimum sample size required to estimate the statistical parameter with a certain accuracy

For the following parameters: mean, standard deviation, rate of occurrence, proportion.

  1. Translation of the objective of reducing the defect rate in terms of the target effect on the statistical parameter.
  2. Determination of the minimum sample size required to detect the effect on the statistical parameter that achieves the objective of reducing the defect rate.

For the following parameters: mean, standard deviation, rate of occurrence, proportion.

  1. Detect the existence of an effect below or above a threshold value
  2. Determine the minimum sample size required to detect an effect at least equal to a certain size
  3. Determine the smallest detectable effect using two samples (resolution)
  4. Determine the detection power of an effect below or above a threshold value, using samples.

Techniques used: two-sample t-test, two-variance test, two-sample Poisson test, two-proportion test

For the following parameters: mean, standard deviation, rate of occurrence, proportion.

  1. Detect if a parameter differs from its reference value
  2. Determine the minimum sample size required to detect if a parameter differs from its reference value
  3. Determine the smallest detectable difference between the value of a parameter and its reference value, using a sample (resolution)
  4. Determine the detection power of a parameter’s deviation from its reference value, using a sample

Techniques used: one-sample t-test, variance test, one-sample Poisson test, proportion test.