Cues available, age, sex, testing centre, model horse viewed, and stimuli direction were entered as predictors in a logistic regression with feeding choice as the response variable (0 = choice incongruent with model; 1 = choice congruent with model; Supplemental Information and Table S1).
To ensure that the association between regret and adaptive choice switching is not confounded with age, we carried out a binary logistic regression with adaptive choice switching on Day 2 as the outcome variable, using age in months and whether participants experienced regret on Day 1 as the predictor variables.
Most of the research used linear regression to analyze vehicle mileage [12, 13, 14, 15, 16, 17, 18], logistic regression to mode choice [19, 20, 21, 22], negative binomial regression to self-selection [23, 24, 25], probit regression on non-work trip generation and vehicle mileage [26, 27], and propensity score matching on neighborhood design and walking trips [28, 29].
Table 8 presents the predicted probability from multinomial logistic regression for the choice of delivery, adjusted for socioeconomic, demographic and cultural factors in India.
The use of cotton nesting material was analysed with binary logistic regression with the choice options, cotton dye colour and date in the analyses.
We further employed logistic regression technique (discrete choice model) to provide a response to examine variables, which are associated with registration of a child at birth.
We carried out a binary logistic regression with adaptive choice switching on Day 2 as the outcome variable and raw BPVS scores, age in months, and whether regret was experienced on Day 1 as predictor variables.
For the purposes of comparison, we utilized both a linear probability model (via ordinary least squares) and logistic regression to estimate choice models using SAS (Version 9.13, Cary, NC, USA), but substantive conclusions are draw from the latter.
The model was estimated using conditional logistic regression, assuming the choices of best and worst were made sequentially [ 24].
Our choice of the generalized logistic regression as the method of choice to study DIF was based on the following.
