## 3 Results and Discussion
### Nanoindentation-Based Basic Mechanical Property Assessment
#### 3.2.2 Statistical Analysis
Although the \"Input Loading Speed\" ranks lower in importance, it still plays a role in forecasting elastic indentation. For the output variable nit (strain rate sensitivity factor), the importance scores are more evenly distributed between the \"Input Initial Indentation Depth\" and the \"Input Loading Speed\", with the former maintaining a slight lead. This indicates that both features are considered relevant  
Figure 5: Bar charts showing the results of the Random Forest analysis. by the model in predicting the strain rate sensitivity factor, with indentation depth being a tad more predictive. Conclusively, the \"Input Initial Indentation Depth\" is identified as the consistently dominant feature across all output variables, suggesting that variations in indentation depth are likely to exert a more significant impact on the modeled material properties than changes in loading speed. Nevertheless, the contribution of the \"Input Loading Speed\" to the prediction process, while lesser, is still deemed valuable. **Method 3: ANOVA Analysis**  
The ANOVA analysis results, organized into Table 2, shed light on the influence of input features such as loading speed and indentation depth on various output variables, specifically focusing on their F-values, \(p\)-values, and statistical significance at a 0.05 significance level. This analysis offers insights into how these input features affect Output Eit (GPa), Output HV, and Output nit (%). From Table 2, it is clear that indentation depth significantly influences Output HV and Output nit (%), as demonstrated by statistically significant \(p\)-values. In contrast, Output Eit (GPa) shows no significant impact from either loading speed or indentation depth. Additionally, loading speed consistently emerges as a non-significant predictor across all output variables, as evidenced by uniformly high \(p\)-values. Delving deeper into the specifics from Table 2, the analysis highlights that the Indentation Depth Category has a profound impact on Output HV, with an F-value of 15.44 and a \(p\)-value of 0.004, indicating a statistically significant difference in hardness values across different indentation depths. However, when examining Output HV's relationship with the loading speed category, the analysis shows an F-value of 0.048 and a \(p\)-value of 0.953, suggesting no significant variance in hardness values across loading speeds. For Output Eit (elastic indentation) in relation to Indentation Depth Category, the high \(p\)-value of 0.769 alongside an F-value of 0.275 indicates that indentation depth does not significantly influence elastic indentation values. Meanwhile, the relationship between Output Eit and loading speed category, with a slightly elevated F-value of 3.306 and a \(p\)-value of 0.108, points to a non-significant but potentially noteworthy trend that could benefit from further exploration. The significance of indentation depth is further underscored in its relationship with Output nit (%), where an F-value of 10.00 and a \(p\)-value of 0.012 reveal a statistically significant difference in strain rate sensitivity across different indentation depths. Conversely, the comparison of Output nit with loading speed category yields an F-value of 0.708 and a \(p\)-value of 0.530, indicating no significant effect of loading speed on strain rate sensitivity. In essence, the ANOVA results underline the paramount importance of indentation depth in affecting Output HV and Output nit (%)while suggesting that Output Eit (GPa) may not be significantly influenced by the examined input features. Despite some input features, like indentation depth, displaying a marked impact on certain outputs, others, such as loading speed, appear to have a minimal influence. The consistently high \(p\)-values associated with loading speed point to its limited effect on the outputs tested.