Abstract
The article deals with one of the possible ways of determining the impact of only one (or selected group) input variable on the output one. When measuring in a real process, the output variable is affected by multiple input variables. The linear dependence of the output variable on the input ones can be determined by the correlation coefficient (multiple correlation coefficient). The influence of only one input variable (or group) can be expressed using the partial correlation coefficient, which is often different from the correlation coefficient. The aim of the paper is to find a way to modify the measured data to match the correlation coefficient of the modified data to the partial correlation coefficient for the original data. This is illustrated by the amount of engine oil soot in the number of hours of operation and the number of days from oil change.
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Outputs of this project LO1202 were created with financial support from the Ministry of Education, Youth and Sports under the “National Sustainability Programme I”.
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Žák, L., Vališ, D., Žáková, L. (2019). One of the Possible Ways to Find Partial Dependencies in Multidimensional Data. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_22
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DOI: https://doi.org/10.1007/978-3-319-97888-8_22
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