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Model for hiring decision support using multiple regression/ correlation Introduction: In 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.Model: Moderating covariate analysis has been used 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 effect 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 varaible.  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 (outcome): constant + beta1X + beta2Z + beta3XZ + epsilonConstant is credentialBeta1 is job description/sX is application outcomeBeta2 experienceZ is goals of the job positionBeta3 is company missionXZ is interaction of outcome and job position aiding in overall goals of the companyEpsilon is limitations or deficiencies (in statistics called as error)We can have more variables, betas or errors in the model. Betas are the continuous indexes that change by time and variables are discrete variables with fixed numerical valueX 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. 

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