The manuscript introduces a framework for presenting the results of the statistical analysis of experiments with multiple responses and multiple factors. We propose a utilisation of factors scaling to enable a transformation that combines main effects, quadratic effects and interactions into a meaningful summary that allows the scientist/experimenter to immediately recognise the most influential factors for a given response. The framework does not replace the thorough evaluation of the results but provides a clear high‐level summary of the relative importance of findings. The visualisation of such factor importance, using intensity heatmaps, allows the immediate understanding of the results across multiple responses that efficiently guides a following detailed analysis of certain responses and factors and contributes in designing subsequent experiments. The methodology is applied to a real industrial experiment and to a simulated data set with a larger number of responses and factors.

中文翻译：

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与科学家交流实验的统计结论

该手稿介绍了一个框架，用于展示具有多个响应和多个因素的实验统计分析的结果。我们建议利用因子缩放，以实现将主效应，二次效应和相互作用结合在一起的有意义的总结，从而使科学家/实验人员可以立即识别出给定响应中最具影响力的因素。该框架并不能替代对结果的全面评估，而是可以对结果的相对重要性提供清晰的高级摘要。使用强度热图可视化此类因素的重要性，可以立即了解跨多个响应的结果，从而有效地指导对某些响应和因素进行以下详细分析，并有助于设计后续实验。该方法应用于实际的工业实验以及具有大量响应和因素的模拟数据集。