Sentence examples for characteristics variable from inspiring English sources

The estimated marginal effects (Appendix) reveal how a discrete change in the farmer characteristics variable from 0 to 1 affects the prediction of a benefit being reported (very significant), holding all other characteristics as constant.

Table 2 Participant characteristics Variable All participants (n = 87) Mean age 72 years Age range 65-90 year % Male 24% % White 39% % Black/African American 54% % Asian 1% % Hispanic/Latino 5% % Other 1% % Medicaid +-/Medicare 37% % Private +/- Medicare 41% % Medicare only 22%.

Table 1 Sample characteristics Variable Mean Socioeconomic characteristics   Household size 4.3 Age of head of household 50.1 Total yearly household expenditure $18,702 Primary education 35.9% University education 6.6% Geographical distribution (by governorate)   Beirut 7.9% Mount Lebanon 37.0% Northern Lebanon 14.7% Bekaa 17.6% Southern Lebanon 12.6% Nabatieh 10.2%.

Demographic characteristics Variable All patients (n = 53) Female Gender 43 (81 %) Age 56 (47–66) Treatment   Clipping 29 (55%%) Coil 22 (42 %) Mechanical Ventilation 22 (42 %) Sepsis/Septic Shock 9 (17 %) Hospital Mortality 6 (11%%) Unfavorable Outcome 26 (49%%) Table 9 (abstract A835).

Boxplots are presented in Figure 3. Table 1 Baseline characteristics Variable Results Age [years] 33 [27.5 to 46.0] Gender, male [n] 8 Systolic blood pressure [mmHg] 130 [119 to 141] Diastolic blood pressure [mmHg] 74 [66 to 87] Mean arterial blood pressure [mmHg] 92 [84 to 102] Heartrate [beats per minute] 70 [65 to 77] Weight [kg] 73 [63 to 78] Height [cm] 173 [170 to 180].

Variable-frequency drives are also categorized by the following load torque and power characteristics: Variable torque, such as in centrifugal fan, pump, and blower applications Constant torque, such as in conveyor and positive-displacement pump applications Constant power, such as in machine tool and traction applications.

Panel A reports summary statistics for the deal characteristics variables.

Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models.

Two separate vectors of beer characteristics variables are included on the right-hand side of (1).

While each data includes richer socio-demographic characteristics variables, only variables that have been assessed identical can be compared due to the characteristics of the matching method.

There are fifteen variables significant in the model, including two sociodemographic variables, five attitudinal factors, one cluster category, and seven trip characteristics variables.