The common intercept value in a linear regression equation is always taken as one. The basis for this is that
The correlation coefficient is used in economics and throughout econometrics to measure the strength of a relationship between two given
According to the Gauss Markov Theorem, if the first six classical assumptions are met within your model, then the estimates
The p-value tells us the lowest level of significance at which we can reject the null hypothesis. What we mean
According to the Gauss Markov Theorem, an Ordinary Least Squares (OLS) model can be considered as the Best Linear Unbiased
Econometrics: Classical Assumption 6 – No explanatory variable is a perfectly linear function of any or all of the other...
Two variables are a perfectly linear function of each other when one variable can be entirely explained by the movement
In econometrics, variance can be described as the spread of the data from the average value of the data set
Econometrics: Classical Assumption 4 – All observations of the error term are entirely uncorrelated with each other.
In econometrics, a fantastic way of observing the relationship between the respective error observations within a given data set
Econometrics: Classical Assumption 3 – There is no correlation between any of the explanatory variables and the error term.
There cannot be any correlation between the explanatory variables present within a given equation and the error term. If it
When the intercept term is present in any given regression equation, it forces the average of the error term to
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