The latter is calculated using a T distribution function that just needs the Degree of Freedom in your model (number of observations minus number of variables) in addition to the t stat. ![]() And, a t stat of 19 translates into a very statistically significant regression coefficient with a P value of 0.000. This is a huge statistical distance away from zero. In other words, your regression coefficient stands 19 Standard Errors away from Zero or from being Null. The t stat is equal to your regression coefficient divided by its Standard Error. In this case your 95% CI for this regression coefficient would range from 0.46 to 0.56. And, the high frontier of this same CI would be: 0.51 + 1.96(Standard Error). In your case, the low frontier of this Confidence Interval would be equal to: 0.51 - 1.96(Standard Error). Sometimes, outputs also give you a 95% Confidence Interval around that coefficient. The standard error of this regression coefficient captures how much uncertainty is associated with this coefficient. ![]() And, together they give you information of how statistically significant is the regression coefficient associated with your variable excesslnst. It has a regression coefficient of 0.51 a standard error of 0.026 a t stat of 19 and a P value of 0.000.Īll those values are related. You have just a single variable in this linear regression:"excesslnst".
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