A Measure of the Information Loss for Inspection Point Reduction

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Kristina Wärmefjord ◽  
Johan S. Carlson ◽  
Rikard Söderberg
Keyword(s):  
Cluster Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Automotive Industry ◽  
Information Loss ◽  
Small Subset ◽  
Efficiency Measure ◽  
Point Reduction ◽  
Highly Correlated

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.

A Measure of the Information Loss for Inspection Point Reduction

2008 ◽  
Author(s):  
Kristina Wa¨rmefjord ◽  
Johan S. Carlson ◽  
Rikard So¨derberg
Keyword(s):  
Cluster Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Automotive Industry ◽  
Information Loss ◽  
Small Subset ◽  
Efficiency Measure ◽  
Point Reduction ◽  
Highly Correlated

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.

scholarly journals Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity

2019 ◽  
pp. 857-862
Author(s):  
Waylson Zancanella Quartezani ◽  
Julião Soares de Souza Lima ◽  
Talita Aparecida Pletsch ◽  
Evandro Chaves de Oliveira ◽  
Sávio da Silva Berilli ◽  
...  
Keyword(s):  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Spatial Information ◽  
Spatial Regression ◽  
Chemical Properties ◽  
Fine Sand ◽  
Black Pepper ◽  
Physical And Chemical

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.

scholarly journals THE EFFECT OF JOB CONDITION, INTERPERSONAL COMMUNICATION, AND PERCEIVED ORGANIZATIONAL SUPPORT ON EMPLOYEE PERFORMANCE OF PDAM COMPANY SURABAYA, INDONESIA

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
Purwanti Purwanti
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Interpersonal Communication ◽  
Organizational Support ◽  
Perceived Organizational Support ◽  
Working Condition ◽  
Employee Performance ◽  
Population Data ◽  
Test Result

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.

scholarly journals Prediction of Phenytoin Dosage in Epilepsy Using Multiple Linear Regression

1982 ◽  
Vol 19 (3) ◽  
pp. 141-144 ◽  
Author(s):  
S P Gray
Keyword(s):  
Body Weight ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Therapeutic Range ◽  
Drug Dosage ◽  
The Body ◽  
Clinical Value ◽  
Number Of Patients ◽  
The Relationship

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.

Quantitative assessment of the seismic net-pay method: A case study

2015 ◽  
Vol 3 (2) ◽  
pp. B25-B36 ◽  
Author(s):  
Ramses G. Meza ◽  
Juan M. Florez ◽  
Stanislav Kuzmin ◽  
John P. Castagna
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Prediction Error ◽  
Quantitative Assessment ◽  
Self Calibration ◽  
Regression Formula ◽  
Direct Use ◽  
Statistical Formula

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.

scholarly journals Pengaruh Current Ratio, Debt to Equity Ratio, Inventory Turnover dan Return on Equity Terhadap Price Earning Ratio

JEMAP ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 238
Author(s):  
Thio Kori Karunia Odelia ◽  
Bonifatius Junianto Wibowo
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Automotive Industry ◽  
Secondary Data ◽  
Financial Statement ◽  
Current Ratio ◽  
Return On Equity ◽  
Inventory Turnover ◽  
Equity Ratio ◽  
Regression Techniques

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.

Geometrical Inspection Point Reduction Based on Combined Cluster and Sensitivity Analysis

Manufacturing ◽  
2003 ◽  
Author(s):  
Johan S. Carlson ◽  
Rikard So¨derberg ◽  
Lars Lindkvist
Keyword(s):  
Cluster Analysis ◽  
Computer Simulations ◽  
Small Subset ◽  
Industrial Case Study ◽  
Point Reduction ◽  
Minimum Number ◽  
Product And Process ◽  
Assurance Process ◽  
Inspection Data

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.

Conversion Factors Relating Two-Group Discriminant Function and Multiple Regression Results

1983 ◽  
Vol 56 (2) ◽  
pp. 475-478
Author(s):  
Edward F. Gocka
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Discriminant Function ◽  
Conversion Factor ◽  
Previous Article ◽  
Conversion Factors

When converting multiple linear regression results to discriminant-function values, the form of coding used in the dummy Y variable will affect scale and location constants in the conversion factor. This note provides two additional conversion procedures to those five given in a previous article. They are for [1,—1] and [1,2] coding of the vector Y, respectively. A computational example is given.

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