It is a measure of how much the variance of the estimated regression

It is a measure of how much the variance of the

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It is a measure of how much the variance of the estimated regression coefficient b k is "inflated" by the existence of correlation among the predictor variables in the model. A VIF of 1 means that there is no correlation among the k th predictor and the remaining predictor variables, and hence the variance of b k is not inflated at all. The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction
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For a given predictor, the t-statistic (and its associated p-value) tests whether or not there is a statistically significant relationship between a given predictor and the outcome variable, that is whether or not the beta coefficient of the predictor is significantly different from zero. The statistical hypotheses are as follow: Null hypothesis (H0): the coefficients are equal to zero (i.e., no relationship between x and y) Alternative Hypothesis (Ha): the coefficients are not equal to zero (i.e., there is some relationship between x and y)
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