## 3 Results  
In the process of collecting experimental data, it is not uncommon to detect data that deviate significantly from the general values of the sample. Such data that do not follow the empirical distribution are called outliers. More precisely, an outlier is an observation that lies outside the overall pattern of distribution [41]. Deviations in the measured values usually occur accidentally, due to measurement errors, or due to non-standard experimental conditions. Such data are not desirable in statistical analysis, because they can lead to poor interpretations of the results [42]. In a practical analysis of empirical data, outliers are any measurement that takes values greater than values within the width of interval 1.5 times the interquartile range above the third quartile, or below the first quartile. When applying the least-squares technique of fitting empirical data, it is recommended to remove all outliers first, to increase the statistical significance of the obtained fitted values. Empirical data on corrosion wear for the NiTi-1 alloy revealed 3, 6 and 8 outliers related to the influence of air, tides and sea, while for the NiTi-2 alloy 2, 7 and 3 outliers were detected for air, tide and sea influence. These data were discarded from our database after further consideration. To interpret the depth of corrosion wear we applied a linear regression model, and thus determined the linear dependence of the corrosion wear value as a function of time. To determine the approximate values of the coefficient \(c_{1}\) defined by Equation (1), it was necessary to calculate the mean values of corrosion wear \(d(t)\) at time \(t\). These mean values were determined for both considered NiTi alloys and all three seawater environments. The sum of squares of the vertical offset from empirical data was minimised, to obtain the best fitted linear function to these mean values. The formed linear models for the NiTi-1 alloy are given by Equations (2)-(4). \[d_{a}^{(1)}(t) =3.03036t, \tag{2}\] \[d_{t}^{(1)}(t) =3.07095t,\] (3) \[d_{s}^{(1)}(t) =24.5832t. \tag{4}\]  
The corresponding linear models formed by the least square fitting procedure for the NiTi-2 alloy are represented by Equations (5)-(7). \[d_{a}^{(2)}(t) =3.36476t, \tag{5}\] \[d_{t}^{(2)}(t) =11.9605t,\] (6) \[d_{s}^{(2)}(t) =25.998t. \tag{7}\]  
The coefficients in Equations (2)-(7) correspond to the mean values for the corrosion rate for the observed NiTi alloy exposed to the influence of the seawater environment, and it is expressed in nm/months. The notation \((x)\) that occurs as the exponent of the variable \(d(t)\) in Equations (2)-(7) and takes values \(x\in\{1,2\}\), represents the ordinal number of the observed alloy, while the notation \(y\) in the index of the variable \(d(t)\) that can take the value \(y\in\{a,\,t,\,s\}\), represents the abbreviated name of the observed seawater environment, i.e., air, tide and sea, respectively. Figures 7 and 8 show a graphical representation of the linear dependence of the corrosion wear \(d(t)\) on the time \(t\) given by Equations (2)-(7) for the NiTi-1 and NiTi-2 alloys under the influence of three different seawater environments. Based on the visual representation of the considered linear models, it can be assumed that the NiTi-1 alloy behaved very similarly in the air and tidal environment, from the point of view of the corresponding corrosion rate values. However, this assumption should be verified further using statistical techniques. Based on Equations (2)-(4), assuming that the monthly corrosion rate was a deterministic value, it can be concluded that the approximate values of parameters \(c_{1}\) areequal to 3.03036 nm, 3.07095 nm and 24.5832 nm for air, tide and sea, respectively, if the considered alloy is NiTi-1. In the case of the NiTi-2 alloy, Equations (5)-(7) give approximate deterministic values of the parameter \(c_{1}\), which takes the values 3.36476 nm/month, 11.9605 nm/month and 25.998 nm/month, for air, tide and sea, respectively.