## 2 Materials and Methods
### Data Obtained by Experiment and Their Formal Interpretation
#### 2.2.3 Correlation Analysis and Regression Dependencies Testing
Such tests are e.g., normality test, homoscedasticity test, autocorrelation test, etc. The basic principle of these tests is the application of two hypotheses, the null \(H_{0}\) and the alternative \(H_{1}\). Based on the relevant test statistic \(V_{stat}\) as a certain statistical quantity belonging to the given test and its comparison with its critical value \(V_{statcrit}\), for the relevant level of significance \(\alpha\), a decision is then made to accept or reject that hypothesis. Usually, if the relevant test statistic exceeds the critical value, the null hypothesis \(H_{0}\) is rejected, and the alternative hypothesis \(H_{1}\) is accepted. The so-called \(p\)-value has a similar application if it is part of the output of the respective test. This, in turn, is compared with the corresponding level of significance \(\alpha\), again for the acceptance or rejection of a particular hypothesis. The \(p\)-value is the probability of obtaining a more extreme result if the null hypothesis is true. If the \(p\)-value is low (e.g., \(<\)=0.05), the alternative hypothesis will be more likely [19, 20, 21, 22, 23]. Table 3 shows basic data of the three test procedures applied in the analysis, specifying the tested property, the null hypothesis \(H_{0}\) and the alternative hypothesis \(H_{1}\). Normality means a normal distribution of residuals. Thus, the Anderson-Darling test is used to verify the fulfilment of this property. Homoscedasticity will be understood as the finite and constant variance \(\sigma^{2}\) of random components and thus also of residuals or that the variance does not change as the independent variable changes. Otherwise, it will be heteroscedasticity. Verification of this property can be realized by the Goldfeld-Quandt test. Autocorrelation will be understood as the state when random components influence each other or the assumption of non-correlation is not met, i.e., independence of random errors, and they show some form of systematic behaviour. The Durbin-Watson test can be used to verify this property. The values of the DW test statistic \(V_{stat}\) range from 0 and 4. A value of 0 means that there is positive autocorrelation between random components, and a value of 4 means negative autocorrelation. A value of 2 indicates that the random components are independent and that there is no autocorrelation between them. The permissible interval for non-correlation is wider, of course, ranging from 1.4 to 2.6. If the autocorrelation is very strong (significant), i.e., there is a high dependence between random components, an inappropriate model has probably been chosen. Significant autocorrelation may also be caused by a violation of the linearity assumption if such a linear model was assumed [15; 16; 17; 18]. In Tables 4 and 5, the selected correlation parameters and results of test procedures used in this analysis are listed. In Table 5, for clarity, the parameters and results of the test procedures of only the selected polynomial models, namely third degree for RD1, and second degree for RD2 and RD3, are presented. All stated values and dependencies were obtained directly in the environment of the MATLAB and MS Excel software systems or indirectly--with the support of these environments.