SLinRA2S: A Simple Linear Regression Analysis Assisting System

Wu, Chien-Ho; Li, Jung-Bin; Chang, Tsair-Yuan

doi:10.1109/icebe.2013.33

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…It is clear that the role of R&D cooperation within the innovation process has recently increased (Xia, Danping, & Yue, 2012). This fact is supported by several studies that confi rm the importance of cooperation -e.g.…”

Section: Introductionsupporting

confidence: 67%

“…Eurostat does not provide number of variables suitable for analyzing, input data from other source will not ensure the causality, therefore, for further research we investigate to analyze the growth of total turnover. Multiple linear regression models take the general form shown below (Chatterjee & Hadi, 2013;Wu et al, 2013):…”

Section: Data Methodology and Analysis Of Research Resultsmentioning

confidence: 99%

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The cooperation between enterprises: significant part of the innovation process – a case study of the czech machinery industry

Stejskal¹,

Meričková²,

Prokop³

2016

E+M

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Section: Introductionsupporting

confidence: 67%

Section: Data Methodology and Analysis Of Research Resultsmentioning

confidence: 99%

The cooperation between enterprises: significant part of the innovation process – a case study of the czech machinery industry

Stejskal¹,

Meričková²,

Prokop³

2016

E+M

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“…Diabetes is there or not, will be the dependent variable. The mathematical equation for Linear regression is given in equation ( 1) [28] below…”

Section: Linear Regressionmentioning

confidence: 99%

Optimizing Timely Diabetes Detection Through Advanced Predictive Modeling and Patient Medical Statistics

Mehan

2023

INDJCSE

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Diabetes has become prevalent across a significant portion of the global population. While medication offers one avenue for disease management, its dosage often escalates with age, eventually necessitating insulin injections. Alternatively, lifestyle adjustments provide another means of control. Detecting diabetes early holds the potential to enhance patient well-being and avert the onset of the disease. This study undertakes a thorough parametric analysis of diabetes, comparing Linear, Multi-Linear, Polynomial, and Logistic Regression Models. The goal is to identify the optimal approach for predicting diabetes. Leveraging a dataset encompassing 768 patients from the National Institute of Diabetes and Digestive and Kidney Diseases, USA, the research considers both Patient Medical Statistics (PMS) and a Combination of Patients Medical Statistics (CPMS), featuring variables such as Pregnancy, Blood Sugar (BS), Blood Pressure (BP), Body Fat (BF), Insulin Level (IL), Body Mass Index (BMI), Diabetes Pedigree Function (DPF), and Age. Evaluation metrics, including Root Mean Square Error (RMSE) and Coefficient of Determination (R2), guide model selection. By employing R2, the ranking of PMS and CPMS is determined to yield a highly efficient model. Empirical findings indicate the Multiple Linear Regression model as presenting the lowest RMSE among all regression models. However, the comparative assessment underscores the suitability of Logistic Regression due to its discrete nature, demonstrating superior accuracy in prediction compared to other models.

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“…Having said that, we believe that a simple, straight forward, flexible and cost effective system that satisfies business decision requirements is better than a software package with functions covering unexpected needs of a business. In line with the spirit of cloud computing and the idea of actively supporting a data analysis process [1], we have developed a web application for multiple regression analysis that can be accessed from internet through browsers. Following the principle of KISS and the morale of public good, the web application is implemented with Java technology and employs open-source ware as building blocks.…”

Section: Introductionmentioning

confidence: 99%

“…The staggering cost of license fees for acquiring commercial software is a sticky issue for a business of any shape and form. Furthermore the proper application of statistical procedures to data analysis for the production of decision information of high quality is another issue that needs to be addressed [1].…”

Section: Introductionmentioning

confidence: 99%

A Web-Based Multiple Regressor for Data Analysis Using R

Wu¹

2014

IJCCE

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Abstract-The production of business analytics inevitably involves applying some statistical procedures to a data set related to the decision problem for a particular scenario. In light of the sheer volume of the data set, it is very often that the computation chores rely very much upon statistical software or others alike. Most of the software for producing business analytics on the market is proprietary with little openness. In this research we exploit the idea of statistical analysis as a service on the cloud and develop a web-based multiple regressor for data analysis. To maintain the portability of the multiple regressor to a maximum degree, the regressor is implemented using Java technology on an open platform with R providing analytic functions. Results of this research have a potential to address issues such as the staggering cost of license fees of commercial packages for SMBs and the geography limitations on where the analytic service is available. This research also reveals the promising future of open-source ware in business applications.

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SLinRA2S: A Simple Linear Regression Analysis Assisting System

Cited by 9 publications

References 5 publications

The cooperation between enterprises: significant part of the innovation process – a case study of the czech machinery industry

The cooperation between enterprises: significant part of the innovation process – a case study of the czech machinery industry

Optimizing Timely Diabetes Detection Through Advanced Predictive Modeling and Patient Medical Statistics

A Web-Based Multiple Regressor for Data Analysis Using R

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