[Multivariate response model with multilevel and its application in the influencing factors of blood pressure].
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To explore the application of multivariate response model with multilevel in the influencing factors of blood pressure.Two response model with three-level was fitted under MLwin 2.02 software.The correlation coefficient between systolic blood pressure (SBP) and diastolic blood pressure (DBP) was 0.949 at region level, and 0.701 at individual level. SBP and DBP level increased with age, while the regression coefficient of age on SBP was significantly higher than on DBP, beta was 0.720 (SBP) and 0.118 (DBP) individually (chi2 = 4284.56, P < 0.001). The DBP and SBP level of male were higher than that of female, while the regression coefficient of gender on DBP was significantly higher than on SBP, beta was 2.208 (SBP) and 3.113 (DBP) individually (chi2 = 31.35, P < 0.001).Multivariate response model with multilevel can be used to analyze the hierarchy structure data, and it is also a good tool to analyze the influencing factors of blood pressure.Cite
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To explore the application of multivariate response model with multilevel in the influencing factors of blood pressure.Two response model with three-level was fitted under MLwin 2.02 software.The correlation coefficient between systolic blood pressure (SBP) and diastolic blood pressure (DBP) was 0.949 at region level, and 0.701 at individual level. SBP and DBP level increased with age, while the regression coefficient of age on SBP was significantly higher than on DBP, beta was 0.720 (SBP) and 0.118 (DBP) individually (chi2 = 4284.56, P < 0.001). The DBP and SBP level of male were higher than that of female, while the regression coefficient of gender on DBP was significantly higher than on SBP, beta was 2.208 (SBP) and 3.113 (DBP) individually (chi2 = 31.35, P < 0.001).Multivariate response model with multilevel can be used to analyze the hierarchy structure data, and it is also a good tool to analyze the influencing factors of blood pressure.
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Regression diagnostics for the multivariate linear model are developed along the lines of the theory for the linear model with univariate response. Internally and externally studentised forms of residuals are given and their distributions found. Distance measures suitable for the assessment of the influence of particular cases on the estimated regression coefficients are considered. The examination of residuals and influence statistics is of great importance in assessing a regression model. Cook & Weisberg (1982) provide an extensive discussion of relevant methods for the linear model with a single response variable. The purpose of the present note is to apply these ideas to the multivariate linear regression problem. Although ordinary least squares estimates of regression coefficients are the same in the multivariate and univariate analyses, there are obvious reasons for carrying out the multivariate analysis to consider simultaneously the different re- sponse variables. One is the possibility that the residual for one response variable in a particular case may not seem to be out of the ordinary in relation to other residuals for that response, but only in relation to the residuals for other responses on the same case. Another is that we may be interested in specifically multivariate aspects of the data. For example, in the problem that prompted this investigation, the main item of interest was the matrix of inter-correlations between five indicators of pollution from sampling stations in the Aegean Sea. This was calculated as the matrix of correlations between the residuals from the regressions of the indicators on covariates including temperature and pH of the seawater. Correlations are particularly vulnerable to distortion by outlying values (Gnanadesikan & Kettenring, 1972), so examination of the multivariate residuals to protect against this was essential. In the following two sections, multivariate residuals and influence measures are presented. Section 4 outlines an application to illustrate the usefulness of the method-
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The multivariate test statistic for multivariate regression may be computed using BMDP6M or SPSS CANCORR. A stagewise multivariate regression procedure is described which is equivalent to the multivariate extension of testing the semi-partial correlation coefficient.
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Based on the analysis method of multivariate linear regression,regression equation of the relationship of college basic courses and professional courses have been set up to make quantitative analysis.the result of statistical analysis offers a scientifical foundation for the teacher's research and management.
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In this paper, the data of CPI, money supply and total social retail goods from December 2019 to September 2020 are taken as samples, and the multivariate linear regression method is used to establish the model and observe the multivariate linear regression relationship. The results show that the linear model with money supply and total social retail goods as independent variables and CPI as dependent variables has high prediction accuracy, that is, money supply and total social retail goods can affect the CPI of residents in China.
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Multivariate linear regression model is regression model with one or more response variable and one or more predictor variable, with each response variable are mutually. In multivariate linear regression model sometimes often found Influential Observation. Influential Observation give most contributing in estimating regression coefficient. For detection Influential Observation on multivariate linear regression model is used Generalized Cook’s Distance. The aim of this research is to detection any or not any Influential Observation on multivariate linear regression model of education indicator in Central Java Province with response variable are Gross Participation Rate (APK), School Participation Rate (APS), and Pure Participation Number (APM) and predictor variable is percentage of population aged 10 years and over who graduated from junior high school. Result from this research can be explained that if the percentage of population aged 10 years and over who graduated from junior high school increase one percent, it will have an impact on increasing gross participation rate the junior high school is 1.7849 % , increasing school participation rate is 1.6275 % and increasing pure participation number is 1.3712 %. Also, from this results were obtained two observations are included Influential observation. Elimination of the two observations are included Influential observation in the multivariate linear regression model of education indicators in Central Java, affects the regression coefficients change only and does not have a major impact on the closeness of the relationship between response variables and predictor variables in the multivariate.
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Multivariate linear correlation analysis plays an important role in various fields such as statistics, economics, and big data analytics. However, there was no compact formulation to define and measure multivariate linear correlation. In this paper, we propose a pair of coupling coefficients, the multivariate linear correlation coefficient (LCC) and linear incorrelation coefficient (LIC), to measure the strength of multivariate linear correlation and linear irrelevance. Pearson's correlation coefficient is a special case of the proposed multivariate LCC for two variables. Based on the proposed multivariate LIC, a compact formula of LIC for linear decomposition is also presented in this paper. The experiment results show that the proposed multivariate LCC is an effective measure for multivariate linear correlation, and a new explanation of determinant is also made from the view of multivariate linear correlation.
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The purpose of this study was to develop a multivariate model with cross-sectional data that defined the decline in VO2max over time, and cross-validate the model with longitudinal data. The cross-sectional sample consisted of 1,608 healthy men who ranged in age from 25 to 70 years. VO2max was directly measured during a maximum Bruce treadmill stress test. Regression analysis showed that the cross-sectional age and VO2max relationship was linear, r = 0.45 and the age decline in VO2max was 0.48 ml/kg/min/year. Multiple regression developed the multivariate model from age, percent body fat (%fat), self-report physical activity (SR-PA), and the interaction of SR-PA and %fat (R = 0.793). Accounting for the variance in percent body fat and exercise habits decreased the influence of age on the decline of VO2max to just −0.27 ml/kg/min/year. This showed that much of decline in maximal physical working capacity was due to physical activity level and percent body fat, not aging. The multivariate equation was applied to the data of the longitudinal sample of 156 men who had been tested twice (Mean AgeΔ = 3.1 ± 1.2 years). The correlation between the measured and estimated change in VO2max over time (ΔVO2max) was 0.75. The results of the study showed that changes in body composition and exercise habits had more of an influence on changes in maximal physical working capacity than aging. The developed model provides a useful way to quantify the changes in physical working capacity with aging.
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