## 6 Conclusions and Limitations  
This study focused on the innovation of interaction methods, user experiments, and quantitative analysis for numerical data input within immersive visual analytics based on MR. Initially, it introduced two multimodal interaction methods: Gesture and Voice and Bimanual Scaling. Subsequently, it conducted an experiment comparing these proposed interaction methods with three established MR input methods. The experiment revealed that the Gesture and Voice method significantly outperforms current industry-standard input methods in both objective and subjective metrics, whereas Bimanual Scaling shows advantages in objective metrics and certain subjective metrics, also demonstrating resilience to environmental noise and greater generalizability. Furthermore, it identified that Pinch-Slider and Touch-Slider interactions necessitate increased hand-eye coordination and finer manipulation, leading to significantly greater cognitive stress in delicate numerical input tasks. The experiment's findings contribute uniquely to overcoming the obstacles associated with inputting precise or complex data in immersive environments, offering guidance for the design of more complex future immersive visual analytics systems. It is anticipated that these interaction techniques will find broader applications, offering users a more natural, efficient, and enjoyable interaction experience. Nevertheless, this study has several limitations. Firstly, the focus was primarily on numerical data input rather than a comprehensive examination of all potential interaction methods in MR environments. For other data types (such as text and graphics), the effectiveness and applicability of the proposed interaction techniques might differ. Hence, the experiment's findings may not be universally applicable to all data input tasks. Secondly, technology implementation limitations could have influenced the experimental outcomes. For instance, speech input accuracy may be contingent on speech recognition algorithms, while gesture and eye-tracking interaction accuracy could depend on hardware performance. Such technological constraints may impinge on user experience and interaction efficiency. Moreover, interactions were tailored to specific hardware (e.g., HoloLens2) and software (e.g., Unity), potentially restricting their applicability across various platforms and devices. Factors such as lighting, noise, and varied hardware and software environments can significantly impact the performance and user experience of the interaction methods. Additionally, while user experience was evaluated through the NASA-TLX questionnaire, this study did not include an evaluation of long-term usage. User adaptations and preferences for interaction styles are likely to evolve over time, especially after prolonged use. Thus, longitudinal user studies could yield more profound insights. Furthermore, this study's limitations include a small sample size of 27 and the narrow age range of participants under 30. Future research should aim to increase the sample size and include a broader age demographic to enhance the findings' generalizability. Lastly, the experiment's controlled conditions may not have fully reflected the real-world applications of MR technologies. Conceptualization, J.C. and J.Z. ; methodology, J.Z. and J.C.; software, J.Z. and T.C. ; validation, J.C. and W.G. ; investigation, J.L. ; resources, J.Z. and J.C.; data curation, T.C. and W.G. ; writing\(-\)original draft preparation, J.Z. and J.L. ; writing\(-\)review and editing, J.C. and J.Z. ; visualization, J.Z. and T.C. ; supervision, J.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the National College Student Innovation and Entrepreneurship Training Program no. 202310295002Z. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to sensitive nature.