In this first stage regression, the ex-post covariance obtained from the zero stage regressions is regressed on a set of instruments.
In the first-stage regression, the exposure is regressed on the IV using linear regression.
In the second-stage regression, the outcome is regressed on fitted values of the exposure taken from the first-stage regression.
In H2, the same binary-dependent variable used in H1, firm's propensity to repeat licensing, is regressed using binary logistic regression on the independent variable, RICV.
In a multivariate regression, a set of outcomes is regressed on a set of predictors.
The association among variables is quantified by R 2 of a multiple linear regression model where cardiorespiratory fitness is regressed onto the confounders.
Table 5 presents selected results from first stage regressions: each of the endogenous variables is regressed on the excluded and included instruments using OLS.
It can be seen from Eq. (6) that the AR model is such a regression model in which x t is regressed on its past values.
Two controls will be selected for each case as follows: First, we will fit a logistic regression model in which asthma outcome is regressed against these matching covariates.
This analysis is based on a regression model in which the standard normal deviate is regressed against the study-specific estimate of the precision of log RR.
Table 3 shows regressions in which the share of and processing exports is regressed on the ratio and other regressors.
