Iycee Charles de Gaulle Summary Model affect the causation, if the causation

Model affect the causation, if the causation

Model for hiring decision support using moderated regression analysisIn business, you are who you hire. People are not your most important asset. The right people are. Predictive analytics can often lead to much more dependable decisions than does instinct alone. Here is a model to support hiring decisions using statistical measures and variables.The model employs moderating covariate analysis with primary independent modifiers and covariate independent modifier. The causal assumption: When x variable is not randomized, then causation must be assumed.  The moderator variable can reversely affect the causation, if the causation between x and y is not presumed.Causal variable relationship: The moderator variable and independent variable, in principal, should not be related.  No special interpretation can be found between a correlated independent and moderator variable.  However, they should not be too highly correlated, otherwise, estimation problems may occur.  The moderator variable must be related to the dependent variable. Measurement: Usually, the moderation effect is represented by the interaction effect between the the dependent and independent variable.  In a multiple regression equation, the moderator variable is as below. In this equation, the interaction effect between X and Z measures the moderation effect.  Typically, if there is no significant relationship on the dependent variable from the interaction between the moderator and independent variable, moderation is not supported.Y= +X+Z+XZ+Y  is outcome is credentials(constant) is job description/sX is application outcome is experienceZ is goals of the job position is company missionXZ is interaction of outcome and job position aiding in overall goals of the company is limitations or deficiencies , and are the continuous indexes that change by time and variables are discrete variables with fixed numerical value. We can have more such indexes. X and Z are specific variables. X is identified in CV of applicant that correlates with application and Z is key requirements, eligibility criteria for job goalsXZ is multiple outcome that is a graduated estimate corresponding to company’s  missions, net worth,  quarterly or annual net profitY or outcome can be a score that is given to each applicant. And make confidence interval for selection of applicant by score    Use 1) ranking system from outcome or 2) category system from confidence interval from outcome for selection criteria for hiringRanking system would use progressive scoring system of the outcome numerical score.Category interval would use matching parameters.