This work deals with analysing the performance of Flat plate solar collector (FPSC) by statistical means with the help of multivariate regression analysis and analysis of variance (ANOVA) approach. The experimental readings of various temperatures (inlet, outlet, plate, and storage tank) were used for the formulation of regression equations for predicting collector efficiency and heat removal rate. Then, the collector efficiency regression model was presented along with ANOVA in a tabular format. The p-value obtained from the regression model of the collector efficiency showed that a significant relationship existed between the input parameters like inlet temperature, outlet temperature, plate temperature, and storage tank temperature. The most significant factor that influences the entire model was found to be inlet water temperature according to ANOVA. The R-sq value for the collector efficiency was more than 99% inferring that the variations of predictor variables had an influence of more than 99% on the response variable (collector efficiency), Whereas, on heat removal rate none of the predictor variables had major impact and effect on. The model had the R-sq (pred) as 67.52 % which can be termed as slightly overfitted and imprecise. Such kind of statistical significance testing, collection of data, and their analysis are used for auxiliary equipment in ships and for the abatement of pollution in ships.
Nagar et al. (Sat,) studied this question.