Interpreting a Summary Table in R (Specifically Estimated Coefficients)

oldwarplanes

New Member
I feel like an idiot for asking this question, but I always get mixed up with this.
For those curious, I'm using the dataset attached to this post.

So I was told in this problem to fit simple regression models with kid_score being the response variable and all the mom categories being the explanatory variables and determine what the coefficients say about the statistical significance between the two variables. So here's on of the models I made:

Code:
mom_iq.lm <- lm(kid_score ~ mom_iq, data=IQ)
And now for the summary:

Code:
summary(mom_iq.lm)

Call:
lm(formula = kid_score ~ mom_iq, data = IQ)

Residuals:
Min      1Q  Median      3Q     Max
-56.753 -12.074   2.217  11.710  47.691

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.79978    5.91741    4.36 1.63e-05 ***
mom_iq       0.60997    0.05852   10.42  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18.27 on 432 degrees of freedom
Multiple R-squared: 0.201,	Adjusted R-squared: 0.1991
F-statistic: 108.6 on 1 and 432 DF,  p-value: < 2.2e-16
So I see that the p-value for mom_iq is quite small, but the question deals with the Estimate Coefficients. What does 0.60997 say about the statistical significance? In a nut shell, I'm really just wondering at what values would you declare the explanatory variables as statistically significant? Also, what do negative coefficients mean in this context? I don't feel like my professor adequately explained it, so any help you guys could give would be awesome! Thanks!