The Research of Multiple Regression Analysis in Rural-Urban Income Disparity

Jian Li,Xiangyu Guo 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.11

The multiple linear regression model contains more than one predictor variable and it shows the relationship among multiple variables. In the existing research field of rural-urban income disparity, the method of multiple regression analysis is mainly employed. But the linear relationship among variables is estimated mainly depending on principal component analysis. Principal component analysis is used to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The principal component analysis is widely used for feature extraction to reveal the most main factors from the multiple aspects. A multiply linear regression model integrating principal components analysis is proposed to address on the income gap between the city and country. The influential factors are given and the analysis results are discussed in this paper. The experimental results on income data from 1990 to 2013 show that the proposed method is effective in predicting the income ratio and analyzing the influential factors.

주성분분석에 의한 재래종 옥수수의 해석

이인섭,박종옥 한국생명과학회 2003 생명과학회지 Vol.13 No.3

육종재료를 얻기 위하여 부산·경남지역에서 수집된 재래종 옥수수 49 계통을 선발하여 본 실험을 실시하였다. 본 시료는 주성분분석을 이용하여 재래종 옥수수를 해석하고 계통분류를 실시하였던 바 다음과 같은 결과를 얻었다. 7 개의 형질을 이용하여 실시한 주성분분석에서는 제 4주성분까지를 가지고 전체 변동의 86.3%를 설명할 수 있었고, 제 2 주성분까지는 전체 변동의 67.4%를 설명할 수 있었다. 주성분에 대한 형질들의 기여율은 형질에 따라 달랐고 상위 주성분에서 켰으며 하위 주성분에서 작았다. 주성분과 형질과의 상관계수는 주성분의 생물학적 의의와 주성분에 대응한 식물체의 형을 명확히 하였는데 제 1 주성분은 식물체의 크기 및 생장기간에 관련된 주성분이었고, 제2주성분은 이삭수와 분얼수에 관련된 주성분이었다. 제 3주성분과 제 4 주성분에서는 형질간에는 유의성이 인정되지 않았다. This study was conducted to get basic information on the Korean local corn line collected from Busan City and Kyungnam Province, a total of 49 lines were selected and assessed by the principal component analysis method. In the result of principal component analysis for 7 characteristics, 67.4% and 86.3% of total variation could be appreciated by the first two and first four principal components, respectively. Contribution of characteristics to principal component was high at upper principal components and low at lower principal components. Biological meaning of principal component and plant types corresponding to the each principal component were explained clearly by the correlation coefficient between principal component and characteristics. The first principal component appeared to correspond to the size of plant and ear, and the duration of vegetative growing period. The second principal component appeared to correspond to the number of ear and tiller. But the meaning of the third and fourth principal components were not clear.

주성분분석을 이용한 한국인 체력연령 추정에 관한 연구

박성빈(Seong Bin Park),김사엽(Sa Yup Kim),정경렬(Kyung Ryul Chung),형준호(Joon Ho Hyeong),옥정석(Jung Sok Oak) 한국발육발달학회 2012 한국발육발달학회지 Vol.20 No.3

The purpose of this study is to estimate the health related physical fitness age using principal component analysis in Korean adults. Method of physical fitness age was suggested using correlation analysis, one-way ANOVA and principal component analysis based on physical fitness test in Korean. Six hundred thirty healthy people were volunteered to participate in the study. Physical fitness status were analyzed consider age groups and trend of physical fitness status by age were identified from results of physical fitness test. Minitab R16.0 was used for statistical analysis. Eight items were determined through correlation analysis. Principal component analysis was used for estimation of physical fitness age. The first principal component was interpreted as health related physical fitness age and principal component scores were calculated with a high correlation to age. Multiple regression analysis was used for estimation of the principal component scores. The principal component scores were converted by the mean and standard deviation of chronobiological age. The converted principal component scores were finally revised by using Z-score. Equations for estimation of physical fitness age were developed. Normality test using Ryan-Joiner test and cross validity analysis were used for verification. As a result, the prediction equation based on principal component analysis was found to obtain significant results. From the results, it could be utilized for evaluation of physical fitness test and development of application.

The Aesthetic Evaluation of Coastal Landscape

김남형,강향혜 대한토목학회 2009 KSCE JOURNAL OF CIVIL ENGINEERING Vol.13 No.2

The evaluation of coastal landscape is absolutely necessary when coastal zone is managed or coastal space is newly created. However, research on coastal landscape is rare and no guidelines exist for coastal landscape planning and management. This paper therefore aims to present techniques for evaluating coastal landscape from the visual perception opinions of respondents through questionnaire survey and multivariate analysis. The questionnaire is evaluated by the 5-point scale of Semantic Differential (SD) method. With Principal Component Analysis (PCA), the following four principal components are extracted and named as principal component loadings: harmony, safety, rurality, and spatiality. All beaches are classified into the 4 groups by cluster analysis. By plotting scores of their principal components in a 2-dimensional semantic space, the aesthetic characteristics of coastal landscape are clarified for every beach. To clarify the interaction between the principal component scores and the SD scores of preference items, multiple regression analysis is performed. Therefore, the relationship between principal components and the preference trends of coastal landscape will be ascertained. If citizen’s universal perceptions about favorite coastal landscape are understood and their needs are considered in the design and building up of coastal structure or space, more visitors will experience enjoyment, comfort and convenience. The evaluation of coastal landscape is absolutely necessary when coastal zone is managed or coastal space is newly created. However, research on coastal landscape is rare and no guidelines exist for coastal landscape planning and management. This paper therefore aims to present techniques for evaluating coastal landscape from the visual perception opinions of respondents through questionnaire survey and multivariate analysis. The questionnaire is evaluated by the 5-point scale of Semantic Differential (SD) method. With Principal Component Analysis (PCA), the following four principal components are extracted and named as principal component loadings: harmony, safety, rurality, and spatiality. All beaches are classified into the 4 groups by cluster analysis. By plotting scores of their principal components in a 2-dimensional semantic space, the aesthetic characteristics of coastal landscape are clarified for every beach. To clarify the interaction between the principal component scores and the SD scores of preference items, multiple regression analysis is performed. Therefore, the relationship between principal components and the preference trends of coastal landscape will be ascertained. If citizen’s universal perceptions about favorite coastal landscape are understood and their needs are considered in the design and building up of coastal structure or space, more visitors will experience enjoyment, comfort and convenience.

Independent Component Analysis를 이용한 fMRI신호 분석

문찬홍,나동규,박현욱,유재욱,이은정,변홍식 대한자기공명의과학회 1999 Investigative Magnetic Resonance Imaging Vol.3 No.2

fMRI의 신호는 매우 다양한 종류의 선호들이 혼합된 상태이며 , 비록 몇 가지의 요소에 대해 모델링하여 그 선호 형태를 추측할 수 있으나 모든 신호를 정확하게 분리하여 뇌신경의 활성화를 반영하는 신호만을 선택적으로 알아 내기는 어려운 일이다. 또한 뇌와 신체의 생리적 현상으로 발생하는 잡음뿐아니라 움직임이나 계기의 잡음은 fMRl의 데이터 분석을더욱 어렵게 한다. 따라서 실제 뇌신경의 활성화를 정확히 나타내는 참고데이터(reference data)를 선택하는 것은 힘든 일이며, 뇌신경의 활성화를 반영하는 의미 있는 여러 신호 형태에 대한 분석은 현재 fMRl의 후처리 (post-processing) 분석 방법에서 하나의 연구 과제라 할 수 있다. 본 연구에서는 prioriknow­-ledge 혹은 참고 데이터가 필요 없는 분석 방법인 Independent Component Analysis (lCA) 를 이용하여 fMRI선호를 분석하였다. ICA는 현재 많이 사용되고 있는 상관 분석 방법에 비해 신호의 형태를 분석하는 데에 보다 효과적일 수 있으며, 지연된 반응 형태를 갖는 신호나 움직임에 의한 신호의 패턴을 분리하여 분석할 수 있다. 한편, ICA만으후 fMRl의 신호에 따라 분석이 효과적이지 못한 경우 Principal Component Analysis(PCA) threshold, wavelet spatial f filtering, 부분적 영상 분석 방법들을 ICA전에 수행 함으로써 보다 효과적인 분석을 수행할 수 있다. ICA는 fMRl 신호의 형태 분석에 효과적인 방법이라고 생각하며, 데이터의 자유도를 감소 하기 위해서는 선 필터링 (pre-filtering) 방법들이 적용될 수 있다. The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

주성분분석을 이용한 사면의 위험성 평가

정수정,김용수,김태형 한국지반공학회 2010 한국지반공학회논문집 Vol.26 No.10

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

주성분 분석 기반의 CPA 성능 향상 연구

백상수(Sang-su Baek),장승규(Seung-kyu Jang),박애선(Aesun Park),한동국(Dong-Guk Han),류재철(Jae-Cheol Ryou) 한국정보보호학회 2014 정보보호학회논문지 Vol.24 No.5

상관관계 전력 분석(Correlation Power Analysis, CPA)은 암호장비에서 알고리즘이 수행될 때 누설되는 전력 소비 신호와 알고리즘의 중간 계산 값의 상관도를 이용하여 비밀키를 추출하는 부채널 공격 방법이다. CPA는 누설된 전력 소비의 시간적인 동기 또는 잡음에 의해 공격 성능이 영향을 받는다. 최근 전력 분석의 성능 향상을 위해 다양한 신호 처리 기술이 연구되어지고 있으며, 그 중 주성분 분석 기반의 신호 압축 기술이 제안되었다. 주성분 분석 기반의 신호 압축은 주성분 선택 방법에 따라 분석 성능에 영향을 주기 때문에 주성분 선택은 중요한 문제이다. 본 논문에서는 CPA의 성능 향상을 위해 전력 소비와의 상관도가 높은 주성분을 선택하는 주성분 선택 기법을 제안한다. 또한 각 주성분이 갖는 특징이 다르다는 점을 이용한 주성분 기반 CPA 분석 기법을 제안하고, 기존 방법과 제안하는 방법의 실험적인 분석을 통해 공격 성능이 향상됨을 보인다. Correlation Power Analysis (CPA) is a type of Side-Channel Analysis (SCA) that extracts the secret key using the correlation coefficient both side-channel information leakage by cryptography device and intermediate value of algorithms. Attack performance of the CPA is affected by noise and temporal synchronization of power consumption leaked. In the recent years, various researches about the signal processing have been presented to improve the performance of power analysis. Among these signal processing techniques, compression techniques of the signal based on Principal Component Analysis (PCA) has been presented. Selection of the principal components is an important issue in signal compression based on PCA. Because selection of the principal component will affect the performance of the analysis. In this paper, we present a method of selecting the principal component by using the correlation of the principal components and the power consumption is high and a CPA technique based on the principal component that utilizes the feature that the principal component has different. Also, we prove the performance of our method by carrying out the experiment.

가중주성분분석을 활용한 정준대응분석과 \\ 가우시안 반응 모형에 의한 정준대응분석의 동일성 연구

정형철 한국통계학회 2021 응용통계연구 Vol.34 No.6

In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations. 본 연구에서는 가중주성분분석으로부터 정준대응분석을 유도하는 Legendre와 Legendre (2012)의 알고리즘을 고찰하였다. 그리고, 가중주성분분석에 기반한 Legendre와 Legendre (2012)의 정준대응분석이 가우시안 반응모형에 기초한 Ter Braak (1986)의 정준대응분석과 동일함을 다루었다. 생태학에서 종의 발현 정도를 잘 설명할 수 있는 가우시안 반응곡선에서 도출된 Ter Braak (1986)의 정준대응분석은 종 패킹 모형(species packing model)이라는 기본 가정을 사용한 후 일반화선형모형과 정준상관분석을 결합시키는 방법으로 도출된다. 그런데 Legendre와 Legendre (2012)의 알고리즘은 이러한 가정없이 Benzecri의 대응분석과 상당히 유사한 방법으로 계산되는 특징을 지닌다. 그러므로 가중주성분석에 기초한 정준대응분석을 사용하면, 결과물 활용에 약간의 유연성을 지닐 수 있게 된다. 결론적으로 본 연구에서는 서로 다른 모형에서 출발한 두 방법이 장소점수(site score), 종 점수(species score) 그리고 환경변수와의 상관관계가 서로 동일함을 보인다.

다변량 통계분석기법을 활용한 금강수계 14개 호소의 수질평가

김진호,주진철,안채민,황대호 대한환경공학회 2021 대한환경공학회지 Vol.43 No.3

Objectives:14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality. Methods:Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering. Results and Discussion:From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants. Conclusions:Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis. 목적:금강수계 내 14개 호소의 수질 특성별 군집화를 위해 요인분석의 결과(factor 1 기반)를 활용해 호소를 군집 및 분류하고 오염원(토지계, 생활계, 축산계)과 수질인자 간 상관분석(correlation analysis)을 통해 오염원이 수질에 미치는 영향을 조사하였다. 방법:금강수계 내 14개 호소의 최근 5년(2014~2018)의 다양한 수질항목 자료를 활용해 군집분석(cluster analysis, CA), 주성분분석(principle component analysis, PCA), 요인분석(factor analysis, FA)을 활용해 수질에 영향을 미치는 주성분을 도출하고, 요인분석을 통해 나온 결과를 바탕으로 실제 오염원과의 상관성을 분석하였다. 결과 및 토의:14개 호소의 요인분석 결과 3~6개의 요인이 추출되었으며 평균 74%의 설명력을 나타냈다. 요인 1에 추출된 수질인자를 바탕으로 호소를 분류한 결과, 질소 요인과 계절 요인으로 분류된 호소는 간월호, 금강호, 삽교호이며, 유기오염과 내부생산으로 분류된 호소는 탑정지, 대호, 석문호, 용담호이며, 유기오염과 내부생산 그리고 인 요인으로 분류된 호소는 부남호, 예당지, 청천지이다. 나머지 보령호, 대청호, 초평지, 송악지는 기타 호소로 분류되었다. 요인분석을 통해 나온 결과와 실제 오염원과의 상관성을 분석한 결과, 토지계, 생활계, 축산계 오염원과 높은 상관성을 나타낸 호소는 간월호, 탑정지, 대호, 부남호, 대청호이며 이들 호소는 특정(토지계, 생활계, 축산계) 오염원의 지속적인 관리가 필요할 것으로 판단된다. 수질과 오염원 관리 측면에서 나쁨으로 평가된 간월호, 삽교호, 석문호는 수질개선을 위한 대책과 오염원 유입 방지 대책이 필요할 것으로 판단된다. 결론:호소의 수질은 유입수, 형태학적 요소, 수문학적 요소, 내부생산요소, 오염원 등의 복합적인 작용으로 인한 결과로서 매우 복잡한 인과관계를 형성하고 있으나 다변량 통계분석 등의 통계학적인 기법을 통해 호소 특성에 맞는 맞춤형 유역 및 수질관리 방안의 도출이 가능하다.

유통과학분야에서 탐색적 연구를 위한 요인분석

임명성 한국유통과학회 2015 유통과학연구 Vol.13 No.9

Purpose – This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology – This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results – PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAFwill suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions – Recommendations are offered for the best factor analytic practice for empirical research.