Stat 350 Spring 2017 Homework 10 - 20 Points + 1 Bonus

Stat 350 Spring 2017 Homework 10 20 Points 1 Point Bonus

Stat 350 Spring 2017 Homework 10 (20 points + 1 point BONUS) Practice problems involve understanding sums of squares and constructing graphs based on Tukey confidence intervals, performing ANOVA and multiple comparison procedures, analyzing data related to watchband weights, and designing a comprehensive medical practice information system with hardware, software, networking, and analytics considerations. The assignment includes theoretical proofs, graphical interpretations, statistical tests, and system design components, aiming to evaluate proficiency in statistical analysis, system architecture, and project planning within a healthcare context.

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Stat 350 Spring 2017 Homework 10 20 Points 1 Point Bonus

The homework assignment encompasses multiple statistical problems and a comprehensive case study on designing a medical practice management system. It aims to evaluate understanding of fundamental statistical concepts, such as sums of squares and multiple comparisons, as well as the application of these concepts in real-world biomedical and business scenarios. The case study involves selecting appropriate software, hardware, networking, and analytical tools, integrating social media, and illustrating the architecture with schematics. The final deliverable must articulate the benefits, challenges, and strategic considerations in implementing a modernized healthcare information system within a constrained budget.

Part 1: Statistical Proof of SST = SSE + SSA

The first part of the homework requires demonstrating that the total sum of squares (SST) equals the sum of the error sum of squares (SSE) and the treatment sum of squares (SSA). This fundamental relationship in analysis of variance (ANOVA) can be proven mathematically based on the decomposition of total variability into components attributable to different sources, confirming the partitions of variability in experimental data. The proof involves algebraic manipulations of the sums of squares formulas and is essential for understanding the basis of ANOVA tests.

Part 2: Graphical Representation Based on Tukey Confidence Intervals

In this section, given sample means and pairwise differences along with their confidence intervals, the task is to construct graphs indicating which pairs of means are significantly different. This involves interpreting Tukey’s Honestly Significant Difference (HSD) results to determine the pairs with confidence intervals that do not include zero, signifying significant differences. The graphs should visually depict the relationships among means, providing an intuitive understanding of the data structure without extensive calculations.

Part 3: Identifying Significant Differences from Multiple Comparisons

Using graphical outputs of multiple comparison procedures, identify all pairs of population means that are significantly different from each other. No additional work or calculations are required, just interpretation of the provided diagrams. This step tests the ability to read multiple comparison results and correctly infer statistical significance, which is critical in experimental data analysis.

Part 4: ANOVA and Post-Hoc Analysis for Watchband Weights

A detailed analysis of weights for gold, silver, and titanium watchbands is presented. The tasks include assessing the validity of the equal variance assumption, completing an ANOVA table, conducting a hypothesis test for differences among means at a 1% significance level, constructing Tukey's 99% confidence intervals to interpret pairwise differences, plotting the results graphically, and providing recommendations for the heaviest watchbands. This comprehensive analysis demonstrates proficiency in multiple statistical techniques essential for quality comparison and decision-making in manufacturing or product analysis.

Part 5: System Design for Medical Practice Management

The case study involves designing an integrated information system for Dr. Decker's multi-location medical practice, with a strict budget of $250,000. The project tasks include selecting appropriate software solutions (cloud-based or on-premise), hardware (computers, servers), networking hardware, and data architecture (databases, data warehouses). Additional considerations include integrating social media into data analytics, developing a system schematic, and assessing the operational benefits. The design must meet requirements such as electronic health records (EHR), appointment scheduling, billing, collections, HIPAA compliance, and analytics, emphasizing creativity and strategic planning within financial constraints.

Part 6: System Architecture and Strategic Analysis

The task is to produce a schematic diagram illustrating the hardware, software, data flow, networking, and personnel involved in the proposed system architecture. This visualization aids stakeholders in understanding component relationships and data pathways. Furthermore, the report must describe the expected tangible and intangible benefits of the system, supported by case studies or best practices, such as improved efficiency, enhanced patient care, compliance, data-driven decision making, and potential growth opportunities, ensuring comprehensive justification of the project’s value.

Part 7: Critical Evaluation and Recommendations

Finally, the analysis should include strategic recommendations for selecting metals for the heaviest watchbands based on statistical comparisons and market requirements. For the healthcare system, decisions regarding SaaS versus shrink-wrapped solutions, and how social media can be harnessed for marketing and data insights, should be articulated. The report concludes with reflections on challenges, limitations, potential extensions, and how the solutions align with industry standards and best practices.

References

• Montgomery, D. C. (2017). Design and Analysis of Experiments. Wiley.
• Johnson, R., & Wichern, D. (2014). Applied Multivariate Statistical Analysis. Pearson.
• Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian Data Analysis. CRC Press.
• Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications. Springer.
• Huang, Y., et al. (2019). Healthcare Data Analytics. Elsevier.
• Sahoo, S., et al. (2020). Cloud Computing for Healthcare. ACM Computing Surveys.
• Kim, J., & Park, S. (2018). Medical System Design Principles. IEEE Reviews in Biomedical Engineering.
• Gartner Inc. (2021). Market Guide for Cloud-Based Healthcare Solutions. Gartner.
• HIMSS Analytics. (2022). Digital Transformation in Healthcare: Strategies and Best Practices.
• Rowe, N. (2020). Data Analytics and Business Intelligence for Healthcare. Springer.