Understanding Patient Data
Data Presentation: NJ OSME Drug-Related Deaths in NJ Counties
This assignment focuses on data presentation and management using drug-related death data from New Jersey counties. The assignment is part of the Understanding Patient Data course and develops practical skills in inspecting, cleaning, organizing, analyzing, and presenting patient-related datasets using Microsoft Excel. Students are required to use the data provided in the file “5.3a Chart on Drug Deaths by NJ County (2015)” and input the county-level information into an Excel spreadsheet. The assignment requires students to inspect and clean the data where necessary, including removing, imputing, and explaining incomplete data entries. Students must also organize and sort the data and obtain descriptive statistics for heroin drug-related deaths and a second variable of their choice. The analysis includes creating descriptive statistics for four variables and comparing the descriptive statistics of heroin with a selected variable. Students must create a copy of the original dataset on a separate worksheet, sort total deaths from largest to smallest, and create a 2-D bar chart and scatterplot. The charts are then used to develop interpretive statements about heroin-related deaths based on the combined analysis of the visualizations. The assignment also requires students to use descriptive statistics to compare the central tendency of three specified variables: Cocaine, Fentanyl, and Oxycodone. A separate worksheet must contain a key or log explaining variable names, abbreviations, and terms used in the dataset. The assignment develops practical skills in Excel-based healthcare data analysis, descriptive statistics, data visualization, interpretation of patient data, and data management. The grading criteria include data input and cleaning, descriptive statistics, sorted data, bar chart creation, scatterplot presentation, and interpretive analysis. The completed assignment must be submitted electronically in Microsoft Excel (.xls or .xlsx) format.
Read Model Answer →
Quality Engineering / Manufacturing Engineering / Statistical Quality Contro
2,500 words
Advanced Quality Engineering: Gauge R&R, Process Capability and Statistical Process Control Analysis
This Advanced Quality Engineering coursework requires students to apply quantitative quality-management techniques to a realistic manufacturing scenario involving the production of precision components. The assessment combines measurement system analysis, process capability evaluation and Statistical Process Control (SPC) to determine whether measurement and manufacturing processes are sufficiently reliable and capable of meeting specified engineering requirements. The first stage focuses on Gauge Repeatability and Reproducibility (Gauge R&R). Students are provided with measurements from ten components, with each component measured twice by two different operators. Using the supplied data and the module's Gauge R&R calculator, students must assess whether the measurement system is capable of evaluating a critical component dimension specified between 115 mm and 116 mm. This section tests the ability to evaluate measurement variation and determine the suitability of a measuring system for quality-control purposes. The second task requires comparison of two CNC machining processes. Historical batch measurements from a Honzaki CNC vertical milling machine are compared with measurements from a replacement Kawda CNC vertical milling machine. Students must calculate the Cp and Cpk process capability indices for both processes, interpret the results and determine which process is better suited to producing components within specification. Recommendations for process improvement and responsibility for corrective action must also be considered. The final task applies Statistical Process Control. Students must construct appropriate X-bar and R control charts, calculate the relevant control limits, plot process performance and investigate potential out-of-control conditions to identify differences between components produced by the two machines. Supporting module material emphasises the distinction between common and special causes of variation, interpretation of abnormal control-chart patterns, process capability and the use of SPC as a feedback mechanism for preventing defective output and improving manufacturing quality. Reference style: keep this as Not specified in the portal. The handbook requires external material to be properly acknowledged, but the uploaded guideline does not prescribe Harvard, APA, IEEE or another named referencing system.
Read Model Answer →