To further support and optimise the production of diving tables for the Armed Forces of Sweden, a research team has developed a new machine called IGUM (Inert Gas UndersökningsMaskin) which aims to measure how inert gas is taken up and exhaled. Due to the new design of machine, the goal of this thesis was to statistically validate its accuracy and verify its reliability. In the first stage, a quality assurance of the linear position conversion key of IGUM in a steady and known gas flow was conducted. This was done by collecting and analysing data in 29 experiments followed by examination with ordinary least squares, hypothesis testing, analysis of variance, bootstrapping and Bayesian hierarchical modelling. Autocorrelation among the residuals were detected but concluded to not have an impact on the results due to the bootstrap analysis. The results showed an estimated conversion key equal to 1.276 ml/linear position which was statistically significant for all 29 experiments. In the second stage, it was examined if and how well IGUM could detect small additions of gas in a dynamic flow. The breathing machine ANSTI was used to simulate the sinus pattern of a breathing human in 24 experiments where 3 additions of 30 ml of gas manually was added into the system. The results were analysed through sinusoidal regression where three dummy variables represented the three additions of gas in each experiment. To examine if IGUM detects 30 ml for each input, the previously statistically proven conversion key at 1.276ml/linear position was used. An attempt was made to remove the seasonal trend in the data, something that was not completely successful which could influence the estimations. The results showed that IGUM indeed can detect these small gas additions, where the amount detected showed some differences between dummies and experiments. This is most likely since not enough trend has been removed, rather than IGUM not working properly.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-208339 |
| Date | January 2022 |
| Creators | Kornsäter, Elin, Kallenberg, Dagmar |
| Publisher | Stockholms universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0018 seconds