Sentence examples for regression students from inspiring English sources

This scale was recoded into a dichotomous variable for use in bivariate logistic regression: Students who were somewhat likely or very likely to choose a STEM career were coded as "1," and those who were somewhat unlikely or very unlikely to choose a STEM career were coded as "0".

In multivariate logistic regression, students from a rural background had a nearly 8-fold increase in the odds of intention to practice rurally after graduation compared to those from urban backgrounds (OR 7.84, 95% CI 4.10, 14.99, P < 0.001).

By linear regression student performance was higher if they had been taught by process experts (regression coefficient 2.7 [0.1, 5.4], p <.05), but not content experts (p =.09).

The multinomial logistic regression takes students who did not enroll as the reference group, the results of which are presented in Table 2.

A single predictor variable significantly contributed to the logistic regression model: students who believed they would not get the desired specialty more often chose Zagreb as a preferred internship workplace (odds ratio 0.32, 95% CI 0.12 0.86).

In the multivariate logistic regression analysis, students' smoking status (OR = 18.25, P < 0.001); gender (OR = 10.10, P < 0.001); friend using Khat (OR = 4.52, P < 0.001); brother using Khat (OR = 2.65, P < 0.001); father using Khat (OR = 1.68, P < 0.05) remained as significant and independent predictors for Khat chewing among higher education students of Jazan region.

When the countries are separated and the background variables analysed in linear regression, most students are grouped in the same categories, which would explain that even the model including all background variables does not explain more of the variation in outcome on N-GAMS (Table 4).

Since I estimate regressions for students in the fourth through eighth grade, observed sibling pairs are generally spaced no more than 5 years apart.

Table 6 Results from a multiple linear regression of student post-test understanding of natural selection   B SE B β Intercept 12.45 1.80 N/A Demographic and cultural/attitudinal measures  1.

Table 4 Results from a multiple linear regression of student incoming acceptance of evolution on all pre-test variables   B SE B β Intercept 2.59.42 N/A Demographic and cultural/attitudinal measures  1.

Table 5 Results from a multiple linear regression of student incoming understanding of natural selection on all pre-test variables   B SE B β Intercept 16.21 2.27 N/A Demographic and cultural/attitudinal measures  1.