Histogram of cladogram and linear discriminant analysis (LDA) score. by Nguyen Thi Hai (12700147)

The results of linear mixed model. by Soheila Qanbari (20455173)

Subjects:

Weighted Linear Regression Analysis of BRI and ASM/BMI. by Wei Huang (36889)

Linear relationships (Spearman’s r (p-value)) the treatment-induced changes in clinical and neurophysiological data. by Soheila Qanbari (20455173)

Subjects:

Multiple linear regression analysis of the RAAS and glucose metabolism indicators before and after treatment in DKD and non-DKD patients. by Ningning Wang (560944)

Multivariable general linear mixed models for retinal thickness measurements. by Roomasa Channa (374127)

Linear regression analysis of agreement between Mx and ORx during LBNP and LBPP experiment, all subjects; hypoxia was excluded due to its effect on cerebral saturation; Mx: transcr... by Marcus Thudium (6441863)

Multiple linear regression analysis of the RAAS system and glucose metabolism indexes in HT and NT patients. by Ningning Wang (560944)

Subjects:

Multivariable general linear mixed model for GC-IPL thickness among participants with diabetes. by Roomasa Channa (374127)

Factors associated with the hemoglobin level stratified by age group: Linear regression. by Alejandro Rojas Chaves (20614814)

Presentation_1_The Bacterial Community Diversity of Bathroom Hot Tap Water Was Significantly Lower Than That of Cold Tap and Shower Water.zip by Chiqian Zhang (2848025)

“…The most abundant families were Burkholderiaceae, Sphingomonadaceae, unclassified Alphaproteobacteria, unclassified Corynebacteriales, and Mycobacteriaceae. A multiple linear regression suggests that the bacterial community diversity increased with water temperature and the age of the simulated PPS, decreased with total chlorine residual concentration, and had a limited seasonal variation. …”

Linear regression analysis for predictors of depressive symptoms (PHQ-9) (<i>n</i> = 2,394). by Aneliana da Silva Prado (15360811)

Temporal trend (1972–2017) of ∑PCB (upper graph) and PCB TEQ (lower graph) in osprey eggs in the whole dataset and in different study areas. by Matti Viluksela (321291)

Parameter estimates of linear mixed effects models for juvenile <i>M</i>. <i>mercenaria</i>, juvenile <i>M</i>. <i>arenaria</i> and juvenile <i>A</i>. <i>islandica</i> mean shell c... by Teagan McMahon (20108036)

BMI groups by SES. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”

BMISES_Data_Part2. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”

Logistic regression for LSES population. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”

Logistic regression for HSES population. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”

Logistic regression for overall population. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”

BMISES_Data_Part1. by Krystal Hunter (6820052)

“…For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P < 0.001). …”