Sentence examples for education log from inspiring English sources

Finally, and in the spirit of the collective model of labor supply, in columns (5) and (6) we present estimates that account for both spousal characteristics (age, education, log wage, and health status) and three conventional distribution factors (the spouses' wage ratio, their age ratio, and their years of education ratio).

Data pertaining to APN, HCPs and unit manager (interview and focus group transcripts, education log, field notes and treatment checklist) will be analyzed.

For metro areas the models include percent with college education, log of family income, and rents and sales for the respective metropolitan area.

Model 1 Adjusted for age of mother, maternal education, log income, maternal BMI, age at NBAS III assessment, parity, and birth weight.

Model 1 of 6 months adjusted for baseline characteristics (maternal age, maternal education, log income, parity, maternal BMI, birth weight), age at BSID assessment on 6 months, weight of infants at 6 months, and HOME score at 6 months.

Multiple logistic regression analysis was performed by adjusting for key covariates such as age, sex, body mass index (BMI), parental history of allergic diseases, maternal education, log-transformed total energy intake, and monthly household income.

In Model 1 which was adjusted for maternal age and education, log-transformed income, maternal BMI, age at NBAS III assessment, parity, and birth weight the state regulation cluster score for Vaishya (i.e., the third-ranked caste group) was lower than the score for Brahmin (coefficient = –3.6; 95% CI = –5.8 to –1.3).

For ages 6, 24, and 36 months, multiple regression models of each BSID II cluster, MDI, PDI, and BRS were adjusted for maternal age, maternal education, log-transformed income, parity, maternal BMI, birth weight, concurrent age at BSID assessment, infant weight, and HOME score (Model 1) and log As levels was further adjusted (Model 2).

But a surprising new study says that families with lower incomes and less education, once they log on to their home computers, on average spend a lot more time there than do others.

To control for potentially confounding background factors, we re-estimate the earlier regressions, adding controls for maternal education and the log of GDP per capita.

Adjusted analyses were performed using linear regression models of outcome on randomised group, adjusting for baseline values of the respective outcome measure, AUDIT-C, age, education, self-efficacy, log (TOT-AL+1), EQ5D and gender.