Pro Manual! Variability Management in Processes and Excel Statistical Analysis Methods

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Applications/Examples of results

We will explain statistical analysis methods that can be immediately applied in practice, such as analyzing manufacturing variation factors and determining the optimal settings for manufacturing condition parameters. - Provision of explanatory materials - In-house training implementation - In-house training (including practical training) - Remote training

Detailed information

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  "I gave up halfway through the statistical analysis guide because it had difficult formulas. Is there a more practical guide with content that can be used immediately in business?" For such engineers, we have created a practical guide that can be used right away. Nowadays, even without using formulas, EXCEL and statistical software can perform the calculations, so engineers are required to understand statistical thinking, how to interpret outputs, their meanings, and improvement methods.

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  When you start tackling statistical analysis, you are first troubled by the difficulty of the terminology. I believe many engineers find the formulas and terms make statistics seem complicated. P-values, significance levels, significant differences, null hypotheses, etc., will be explained clearly using diagrams.

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  In the manufacturing field, defects occur due to various types of variability. Most of these defects arise because variability cannot be effectively managed. How can we control variability, or manage parameters so that we are not affected by variability even when it occurs? We will clearly explain practical statistical analysis methods for the manufacturing industry, such as process capability (Cpk), standard deviation, control charts, and design of experiments.

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  This explains commonly used statistical analysis methods that are useful in practical applications such as quality management and improvement activities. It covers essential topics that engineers should at least understand, including hypothesis testing, analysis of variance, correlation analysis, regression analysis, and multivariate analysis.