Correlation and regression analysis in assessing the relationship between water indicators: a brief description of long-term measurement data from biosensors - Yeldos, Altay; Bazarbay, Lashin; Ozhikenov, Kassymbek; Dosbayev, Zhandos; Nakan, Ulantay; Kalpeyeva, Zhuldyz (Technology audit and production reserves, 2025)
The object of the study is the method for assessing the relationship between the results of long-term observations of water parameters obtained by the method of combined measurements by a biosensor system. The biosensor system is designed for the combined measurement of five water parameters based on physical value sensors. In the paper, the problem under consideration quite fully levels out a significant limitation of the known solutions designed for the simultaneous measurement of three or four water parameters. Existing approaches in their structure combine less than five biosensors-sensors, which significantly limits the simultaneous measurement of five water parameters. One of the main and principal results of the paper is the development of a statistical model for assessing the relationship between the combined measurements of five water parameters. It was revealed that among the five measured parameters, the most influential predictor for acidity, conductivity, turbidity and oxidation-reduction potential is water temperature. The established significant and non-random relationship between the parameters is mainly associated with the effect of temperature on the physical processes occurring with an increase and decrease in water temperature depending on the observation time. These estimates demonstrate a higher, statistically significant relationship between the measurement information data. This is achieved by implementing the method of aggregate measurement of water parameters: temperature, acidity, turbidity, conductivity, oxidation-reduction potential. The efficiency of the statistical model is confirmed by calculating the correlation coefficient based on the Pearson method and the coefficients of determination and reliability of the model. The regression model can be used in practice in developing new or improving known measuring systems and control devices to increase the reliability and effectiveness of water quality control.