quadratic coefficient decreases from 1.671e-05 to 1.212e-05.

> df <- data.frame(n = c(500,1000,1300,2000), t = c(3.62,12.77,20.77,49.07)) ; fit <- lm(t ~ I(n ^ 2), data = df) ; summary(fit)

Call:
lm(formula = t ~ I(n^2), data = df)

Residuals:
       1        2        3        4
 0.05496  0.11786 -0.24227  0.06946

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.360e-01  1.594e-01   3.362   0.0782 .
I(n^2)      1.212e-05  7.145e-08 169.576 3.48e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2005 on 2 degrees of freedom
Multiple R-squared:  0.9999,	Adjusted R-squared:  0.9999
F-statistic: 2.876e+04 on 1 and 2 DF,  p-value: 3.477e-05