• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.953-958
• /
• 2013

This study suggests a measurement error model based on skewed normal distribution instead of normal distribution to identify slope parameter properties in a simple liner regression model. We prove that the slope parameter in a simple linear regression model is underestimated.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.84-92
• /
• 2021

Care services are a significant asset in human life. Care in its overall nature focuses on human needs and covers several aspects such as health care, homes, personal care, and education. In fact, care deals with many dimensions: physical, psychological, and social interconnections. Very little information is available on estimating the cost of care services that provided to orphans and abandoned children. Prediction of the cost of the care system delivered by governmental or non-governmental organizations to support orphans and abandoned children is increasingly needed. The purpose of this study is to analyze the care cost for orphanage organizations in Saudi Arabia to forecast the cost as well as explore the most influence factor on the cost. By using business analytic process that applied statistical and machine learning techniques, we proposed a model includes simple linear regression, Naive Bayes classifier, and Random Forest algorithms. The finding of our predictive model shows that Naive Bayes has addressed the highest accuracy equals to 87% in predicting the total care cost. Our model offers predictive approach in the perspective of business analytics.

• Korean Journal of Health Education and Promotion
• /
• v.13 no.2
• /
• pp.130-166
• /
• 1996

As compared with body height and body weight by ages and sexes, by means of the data reported under other researchers from 1967 to 1994 for 33 years, this study obtained the estimate value of body height and body weight by ages and sexes for the same period, and figured out prediction value of body height and body weight in the ages of between 6 and 14 from 1995 to 2000. These surveys and measurements took for one year from October 1st 1994 to September 30th. As shown in the 〈Table 1〉, in order to calculate the establishment, estimate value and prediction value of the chronological regression model of body height and body weight, by well-grounded 17 representative research papers, this research statistically tested propriety of liner regression model by the residual analysis in advance of being reconciled to simple liner regression model by the autonomous variable-year and the subordinate variable-body weight and measured prediction value, theoretical value from 1962 to 1994 by means of 2nd or 3rd polynomial regression model, with this redult did prediction value from 1995 to 2000. 1. Chronological Change of Body Height and Body Weight The analysis result from regression model of the chronological body height and body weight for the aged 6 - 16 in both sexes ranging from 1962 to 1994, corned from the 〈Table 2-20〉. On the one hand, the measurement value of respective researchers had a bit changes by ages with age growing, but the other hand, theoretical value, prediction value showed the regular increase by the stages and all values indicated a straight line on growth and development with age growing. That is, in case of the aged 6, males had 109.93cm in 1962 and females 108.93cm, but we found the increase that males had 1I8.0cm, females 1I3.9cm. In theoretical value, prediction value, males showed the increase from 109.88cm to 1I7.89cm and females from 109.27cm to 1I5.64cm respectively. There was the same inclination toward all ages. 2. Comparision to Measurement Value and Prediction Value of Body Height and Body Weight in 1994 As shown in the 〈Table 21〉, in case of body height, measurement value and prediction value of body height and body weight by ages and sexes almost showed the similiar inclination and poor grade, in case of body weight, prediction value in males had a bit low value by all ages, and prediction value in females had a high value in adolescence, to the contrary, a low value in adult. 3. Prediction Value of Body Height and Body Weight from 1995 to 2000 This research showed that body height and body weight remarkably increased in adolescence but slowly in adult. This study represented that Korean physique was on the increase and must be measured continually hereafter.

• Asia pacific journal of information systems
• /
• v.23 no.3
• /
• pp.25-53
• /
• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.