Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks

1994 ◽  
Vol 66 (15) ◽  
pp. 2480-2487 ◽  
Author(s):  
Matthew D. Wessel ◽  
Peter C. Jurs
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ion Mobility ◽  
Structural Information ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Computational Neural Networks

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

scholarly journals The Association of Circulating MiR-29b and Interleukin-6 with Subclinical Atherosclerosis

2017 ◽  
Vol 44 (4) ◽  
pp. 1537-1544 ◽  
Author(s):  
Yu-qing Huang ◽  
Jie Li ◽  
Ji-yan Chen ◽  
Ying-ling Zhou ◽  
An-ping Cai ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Interleukin 6 ◽  
Subclinical Atherosclerosis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Group ◽  
Study Group ◽  
Circulating Levels

Background/Aims: Although it is widely acknowledged that atherosclerosis is mainly a chronic inflammatory process, in which both miR-29b and interleukin-6 (IL-6) play multifaceted roles, the association between miR-29b and IL-6 remains unknown. The aim of the present study was to explore the relationship between miR-29b and IL-6 and to test whether circulating levels of miR-29b and IL-6 could predict atherosclerosis. Methods: A total of 170 participants were divided into two groups according to carotid intima-media thickness (CIMT): study group (CIMT ≥ 0.9mm) and control group (CIMT < 0.9mm). Levels of circulating miR-29b and IL-6 were measured by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The association of miR-29b and IL-6 levels with CIMT was assessed using Spearman correlation analysis and multiple linear regression analysis. Results: The study group showed higher miR-29b levels (31.61 ± 3.05 vs. 27.91 ± 1.71 Ct, p < 0.001) and IL-6 levels (3.40 ± 0.67 vs. 2.99 ± 0.37 pg/ml, p < 0.001), compared with the control group. CIMT was positively correlated with miR-29b (r = 0.587, p < 0.001) and IL-6 (r = 0.410, p < 0.001), and miR-29b levels were also correlated with IL-6 (r = 0.242, p = 0.001). Multiple linear regression analysis also showed that CIMT was positively correlated with miR-29b and IL-6. After adjustment for age, body mass index, systolic blood pressure, total cholesterol and C-reactive protein, CIMT was still closely correlated with miR-29b and IL-6. The combination of miR-29b and IL-6 (AUC = 0.901, p < 0.001) offered a better predictive index for atherosclerosis than either miR-29b (AUC = 0.867, p < 0.001) or IL-6 (AUC = 0.747, p < 0.001) alone. Conclusion: Circulating levels of miR-29b and IL-6 may be independently correlated with subclinical atherosclerosis, and may serve as novel biomarkers for the identification of atherosclerosis.

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