Suppose that a worker’s productivity at manufacturing firms (prod) depends on two factors, hours of training (train) and worker’s efficiency. prod = a + b1 *train + b2 *efficiency + u Assume that this equation satisfies the Gauss-Markov assumptions. If less efficient workers tend to be required to take on-job-training, what is the likely bias in the estimated coefficient of train from the simple regression of prod on train? (i.e. is it under or overestimated) Please explain your answer.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Suppose that a worker’s productivity at manufacturing firms (prod) depends on two factors, hours of training (train) and worker’s efficiency.
prod = a + b1 *train + b2 *efficiency + u
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