I understand that the output of, for example,
summary(lm(Fertility ~ . , data = swiss)) is a table of $\hat\beta$s, standard errors, t-statistics, and p-values for two-sided tests of the null hypothesis that each parameter is equal to 0.
But, what should I actually call this method of evaluating a linear model when presenting it to non-statisticians? If I say I used a t-test, it will confuse the hell out of them.
We have the term ANOVA to describe testing hypotheses about coefficients by using F-statistics when we don't care about the direction of change. Is there a corresponding concise, unambiguous term for testing hypotheses about linear coefficients by using their t-statistics when we do care about the direction of change?