The Estimated Standard Error and the t Statistic (cont.) A large value for t (a large ratio) indicates that the obtained difference between the data and the hypothesis is greater than would be expected if the treatment has no effect.
You should delete it and run the analysis again to obtain a model that show only all significant variables.
Generally, any t-value greater than +2 or less than – 2 is acceptable. The higher the t-value, the greater the confidence we have in the coefficient as a predictor. Low t-values are indications of low reliability of the predictive power of that coefficient.
If the p-value that corresponds to t is less than some threshold (e.g. α = . 05) then we reject the null hypothesis and conclude that there is a statistically significant relationship between the predictor variable and the response variable.
Simple word: No, you never throw away any variables that are not significant. Even if the significance level of all the independent variables shows that the variables are insignificant, it does not mean that any of those independent variables won't affect the response variable at all.
But in some cases, even insignificant variables must be kept. Probably the easiest way, but not necessarily the best, would to remove the most insignificant variable one at a time until all remaining variables are significant. Hope this helps! Forward, backward & stepwise variable selection are invalid.
The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis.
Higher values of the t-value, also called t-score, indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score indicates that the groups are different. A small t-score indicates that the groups are similar.
For each test, the t-value is a way to quantify the difference between the population means and the p-value is the probability of obtaining a t-value with an absolute value at least as large as the one we actually observed in the sample data if the null hypothesis is actually true.
So if your sample size is big enough you can say that a t value is significant if the absolute t value is higher or equal to 1.96, meaning |t|≥1.96.
A negative t-value indicates a reversal in the directionality of the effect, which has no bearing on the significance of the difference between groups.
To find the t value:Subtract the null hypothesis mean from the sample mean value.Divide the difference by the standard deviation of the sample.Multiply the resultant with the square root of the sample size.