The other aspect requested by Dr. Hill was separate control charts with a 95% confidence level for each metric – with all calculations used being shown in an accompanying Word document (Table 1). The mean value calculated using this formula “(sum xi/n)” for late surgery cases resulted in 182 (182 = 1825/10); whereas the UCL (Upper Control Limit) equated to 257 and LCL (Lower Control Limit) equated to 107 when factoring into account factors such as Sigma Value (+1 Standard Deviation = 51) and Mean Value (182). As such any values above 257 can be considered out-of-control points which would require action or investigation; while those below 107 can also be deemed out-of-control points but are more likely indicative of improved performance compared against expected results or standards – although they should still be investigated nonetheless just like their counterparts above UCL.

Similarly looking at Figure 3 we see that same logic applies regarding our second metric – Total Number Of Surgeries – where mean value resulting from our formula is 1960; Upper Control Limit is 2840; Lower Control Limit is 1080 whilst +1 standard deviation equates to 326 giving us fairly wide margin overall between what we deem acceptable performance point versus those which require further investigation or action (-1080 & +2840 respectively).

In conclusion two main observations can be made based on our analysis: firstly there appears significant upward trend when it comes down to both overall surgery numbers and similarly Late Surgeries Patient Numbers; secondly whilst some variation must be expected given nature of type surgery and individual circumstances underlying them – having said this any readings outside 1080 – 2840 range should be monitored closely given nature higher risk associated with working outside these parameters even if individual case taken potentially could warrant such deviation due its severity or complexity etc..

Solution Two
Dr Amanda Guzik asked you analyze 5 year influenza data collected at Forest Medical Center in Lake Park Illinois so let us take closer look into her results by creating Run Chart followed by setting up Control Chart along 95% Confidence Level complete with showing all calculations used behind it so one may draw conclusions regarding influenza trends across patients treated within medical centre during past 5 years period under consideration here today . In essence we set ourselves goal understand better underlying patterns related fluctuation not only how many cases occurred annually but also why did they occur?
By using Excel sheet provided alongside having completed necessary formulas therein Run Chart become available ,this one visualizes represented data rather nicely thus allowing us gain better understanding about marked fluctuations reoccurring each year thoughout designated study period ranging anywhere between 100 flu related patient visits back end 2015 all way through peak observed mid 2017 prior gradually declining towards 189 figure reported towards end 19th year under review here today .In addition one notices sudden decrease following initial increase clearly visible across 2016 /17 interval before slowly tapering off afterwards suggesting presence certain external factor influencing dropping rates seemingly out blue yet necessitating further investigations beyond scope this particular report however important indicator nonetheless marking anecdotal evidence worth noting down here when considering previously mentioned patterns [see Fig 1].
Control Chart allows determining variability present within dataset hence helping identifying potential outlier points far easier regardles size sample population analysed thus adding extra layer context helping answering question why exactly set phenomenon occurs defining root cause much clearer manner [see Table 2]. With regards presented dataset values obtained shall enable establishing upper limit value equal 2623 patients per annum meanwhile lower boundary point falls 897 mark plus taking into calculation +/-Standard Deviations figures received amounted 1196 respectively 2530 though naturally depending upon required accuracy figures might slightly change despite fundamental principle remain unchanged regardless thereby providing useful baseline going forward when attempting analyse similar events later down line . Hence based on studies recently conducted two key findings revealed read follows : Firstly Flu Related Visits appear follow regular increasing pattern ensuring peaks occur roughly every two years give take accounting unforeseen events actively influencing situation either positively negatively impacting outcome ; Secondly patient visits tend stay within reasonable boundaries namely 897 –2623 allowing proper forecasts being generated enabling adequate preparations put place whenever eventualities arise hence minimising unexpected interruptions workflows maximising efficiency instead .