As we have 100 observations, so the degrees of freedom df= number of observations – 1 = 99.
With 95% of confidence interval and df=99, p-value =0.05, so the critical t-value is ±1.984.
Based on corresponding t-values and p-values we can tell which independent variables are good
for our model. This can be done by comparing the critical t-value against the calculated t-value,
the calculated t-value should not fall in the interval of critical t-value, also checking the
significance level of the independent variables whose p-value<0.05. This helps in interpreting
which independent variables to be considered as a good fit for our model.
Upon comparison based on the above discussion, we can understand that the independent
variables College GPA, Major GPA, GRE specialty, GRE verbal, GRE quantitative, 1 Letter of
recommendation, 2 Letter of recommendation, emotional stability, Marital status, interpersonal
skills, Hostility, selectors impression of applicants have p-value more than 0.005 and t-calculated
values fall in the interval of ±1.98 which is the t-critical value. Hence, indicating that these
variables are not statistically significant and may overfit our model. So, there is a need to drop
these variables and rerun our regression model without considering those variables which
violates our assumption of statistical significance.
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