A Measure of the Information Loss for Inspection Point Reduction

Author(s):  
Kristina Wärmefjord ◽  
Johan S. Carlson ◽  
Rikard Söderberg

Since the vehicle program in the automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In many situations, a large number of inspection points are measured, despite the fact that only a small subset of points is needed. A method, based on cluster analysis, for identifying redundant inspection points has earlier been successfully tested on industrial cases. Cluster analysis is used for grouping the variables into clusters, where the points in each cluster are highly correlated. From every cluster only one representing point is selected for inspection. In this paper the method is further developed, and multiple linear regression is used for evaluating how much of the information is lost when discarding an inspection point. The information loss can be quantified using an efficiency measure based on linear multiple regression, where the part of the variation in the discarded variables that can be explained by the remaining variables is calculated. This measure can be illustrated graphically and that helps to decide how many clusters that should be formed, i.e., how many inspection points that can be discarded.

Author(s):  
Kristina Wa¨rmefjord ◽  
Johan S. Carlson ◽  
Rikard So¨derberg

Since the vehicle program in automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In many situations, a large number of inspection points are measured, despite the fact that only a small subset of points is needed. A method, based on cluster analysis, for identifying redundant inspection points has earlier been successfully tested on industrial cases. Cluster analysis is used for grouping the variables into clusters, where the points in each cluster are highly correlated. From every cluster only one representing point is selected for inspection. In this paper the method is further developed and multiple linear regression is used for evaluating how much of the information that is lost when discarding an inspection point. The information loss can be quantified using an efficiency measure based on linear multiple regression, where the part of the variation in the discarded variables that can be explained by the remaining variables is calculated. This measure can be illustrated graphically and that helps to decide how many clusters that should be formed, i.e. how many inspection points that can be discarded.


Author(s):  
Waylson Zancanella Quartezani ◽  
Julião Soares de Souza Lima ◽  
Talita Aparecida Pletsch ◽  
Evandro Chaves de Oliveira ◽  
Sávio da Silva Berilli ◽  
...  

There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.


2020 ◽  
Vol 7 (01) ◽  
Author(s):  
Purwanti Purwanti

The aims of this study is to examine the effect of working condition, Interpersonal Communication and Perceived Organizational Support on performance employment of PDAM  company, Surabaya, Indonesia. Methode used in this research is descriptive Explanatory which is a method that explains causal relationships between the variables observed. This research is limited by data collected from a sample of the population to represent the whole population. Data analyzed by multiple linear regression to, T-test, and F test, with SPSS program. The test result of multiple regression show that every increasing Working condition, Interpersonal Communicationa and perceived organizational support will increase performance of the employes. The results of Hyphothesis thest shows that as a simultaniously there were significant effect between Working condition, Interpersonal Communicationa and perceived organizational support to employee performance, eventhough as a partially that Working condition, and Interpersonal Communicationa are significant effect to employee performance but Perceived Organizational Support has no significant effect to employee performance.


Author(s):  
S P Gray

Analysis of plasma phenytoin in a group of patients treated for epilepsy showed that only 36% had values in the therapeutic range. The relationship between plasma phenytoin, body weight, and daily dosage of the drug were explored, and the data were analysed by multiple regression. The resultant equation, relating all three factors, was used to optimise drug dosage, and the importance of using the body weight of the patient before starting a phenytoin regimen is emphasised. An increase in the number of patients with plasma phenytoin in the therapeutic range was achieved, and the clinical value of being in that range is shown.


2015 ◽  
Vol 3 (2) ◽  
pp. B25-B36 ◽  
Author(s):  
Ramses G. Meza ◽  
Juan M. Florez ◽  
Stanislav Kuzmin ◽  
John P. Castagna

We applied the seismic net-pay (SNP) method to an oil discovery and predicted thicknesses consistent with the actual thicknesses at the wellbore locations. This was accomplished by applying the method in a self-calibrating mode that did not require the direct use of well information. For net-pay estimation under a self-calibration scenario, the SNP method thickness estimates proved to be more accurate (mean absolute prediction error at well validation locations under [Formula: see text]) than estimates from a reflectivity-based detuning method ([Formula: see text]) or multiple linear regression ([Formula: see text]). Statistical [Formula: see text]-tests indicated that the correspondences of the predicted thickness estimates with actual net-pay values for the SNP and reflectivity methods (F approximately 5.5–6 for both) were statistically significant, whereas the multiple regression results did not prove to be statistically significant.


JEMAP ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 238
Author(s):  
Thio Kori Karunia Odelia ◽  
Bonifatius Junianto Wibowo

This research was conducted to examine the effect of Current Ratio, Debt to Equity Ratio, Inventory Turnover and Return on Equity on Price Earning Ratio at the automotive industries in Indonesia.  The samples of this research were 12 automotive industry companies which go public.  The data of  this research   was secondary data, which obtained from financial statement of 12 automotive industry companies.  Those data was collected from www.idx.co.id.  Then, The data was analysed by multiple linear regression techniques with the t test. The result shows that the Current Ratio, Debt to Equity Ratio, Inventory Turnover and Return on Equity variables have no effect on Price Earning Ratio.  It means that Price Earning Ratio is not determined by Current Ratio, Debt to Equity Ratio, Inventory Turnover and Return on Equity, but by other factors such as business costs, economic and monetary conditions.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2294-2294
Author(s):  
Stéphanie Forté ◽  
Florence Blais ◽  
Mathias Castonguay ◽  
Nafanta Fadiga ◽  
Richard Ward ◽  
...  

Background: Sickle cell disease (SCD) patients are at significant risk for stroke and silent cerebral infarcts. At least 33% of adults have cognitive dysfunction. However, access to specialized assessments is limited, and there is currently an unmet need for a fast, easy to administer, screening tool for cognitive impairment in SCD. The Rowland Universal Dementia Assessment Scale (RUDAS) is a 6-item task-based questionnaire that evaluates executive function, memory, language, visual-spatial function, praxis and judgment. It has been validated in many cultures and neurocognitive diseases other than SCD. Hypothesis: Poor RUDAS performance is associated with the presence of SCD complications independent of age, socioeconomic and education factors. Methods: Study design: cross-sectional, two adult sickle cell comprehensive care centers in Canada. Inclusion criteria: out-patients ≥18 years-old; all SCD phenotypes. Exclusion criteria: inability to obtain informed consent and/or follow study instructions. Intervention: RUDAS was administered twice, 2-4 months apart, in French or English, based on the patient's preference. Survey on demographics and patient-reported outcomes (PROMIS® tools for Depression and Anxiety) were completed. Baseline characteristics, SCD complications, and laboratory results were collected. Statistical plan: t-tests, Fisher exact and chi-squared tests, for continuous and discrete variables respectively, were performed to identify possible association between RUDAS and biologic, socioeconomic, and cultural factors, SCD related complications, comorbid conditions, laboratory parameters, and use of disease-modifying therapy (Table). Associations with univariate P <0.05 were included in the multiple linear regression model. Multicollinearity was assessed. Results: Of the 252 participants, 92 were from Centre Hospitalier de l'Université de Montréal in Montréal, 160 were from University Health Network in Toronto. Median age at time of survey was 31.5 years (IQR 25-44). Female to male ratio was 1.15. Sickle genotype was distributed as follows: SS 55% (N=138), SC 32% (N=80), other sickle genotypes 13% (N=34). Median RUDAS score was 26 (IQR 24-28), mean score ± standard deviation was 26.0±2.9. Suspected cognitive impairment (defined as RUDAS score <23/30) was found in 12% (N=29) of the participants. On univariate analysis, RUDAS score declined significantly with age (P<0.001), lower eGFR (P<0.001), lower systolic blood pressure (P=0.022), and lower reticulocyte count (P=0.007), while higher level of education (P=0.012), employment and/or active enrolment in a study program (P<0.001), and diagnosis of depression (P=0.009) were predictive of higher RUDAS scores (Table). Reticulocyte count, eGFR, and highest level of education remained independent predictors of RUDAS score on multiple linear regression (P=0.003, <0.001, and 0.001 respectively; see Table for effect size). Center, language of administration, age and diagnosis of depression were not associated with RUDAS score on multiple regression. R2 of the model was 0.323. All variance inflation factors in the model were <2.0. Conclusions: Reticulocyte count and eGFR, but not SCD genotype, being independent predictors of RUDAS suggests disease phenotype may contribute to neurocognitive decline and deserves further exploration. RUDAS does not appear to be influenced by age, language of administration, socioeconomic status, and depression, on multiple regression with mild collinearity. Interestingly, education was independently associated with RUDAS score, despite previous studies showing RUDAS was not biased by education. Recruitment is ongoing at two additional sites to further delineate these relationships and to explore the role of silent cerebral infarct in neurocognitive decline in SCD patients. RUDAS may be a promising tool to identify the patients at higher risk for cognitive impairment who may benefit from access to specialized neurocognitive, educational and social interventions. Disclosures Kuo: Agios: Consultancy; Alexion: Consultancy, Honoraria; Apellis: Consultancy; Bioverativ: Other: Data Safety Monitoring Board; Bluebird Bio: Consultancy; Celgene: Consultancy; Novartis: Consultancy, Honoraria; Pfizer: Consultancy.


Author(s):  
Johan S. Carlson ◽  
Rikard So¨derberg ◽  
Lars Lindkvist

Analyzing inspection data is an important activity in the geometry assurance process, which provides vital information about product and process performance. Since inspection is related to a significant cost, it is desirable with an intelligent inspection preparation where the motive is to gather as much information as possible about the product and the process with a minimum number of inspection points. In many situations, a large number of inspection points are used despite the fact that only a small subset of points is needed. The reason for this redundancy is that most systems have only a few principal causes affecting groups of variables. In this paper, we use methods of cluster analysis to find these natural groupings of inspection points and to select one representing point from each cluster. Furthermore, if the relationship between some of the process parameters and inspection points are known from experiments or from computer simulations, then the cluster analysis is combined with sensitivity-based reduction. In this way, an efficient reduced inspection plan is built up. The practical relevance of the proposed methodology for reduction is verified on an industrial case study and by computer simulations.


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