No meaningful relationship! (see code below.) However, big caveat here that we had to guess on some of the samples because many studies do not report how many subjects or meals were treated (e.g. they report how many restaurants or days were assigned to treatment and control but didnβt count how many people participated)
> summary(lm(d ~ total_sample, data = dat))
Call:
lm(formula = d ~ total_sample, data = dat)
Residuals:
Min 1Q Median 3Q Max
-0.59897 -0.13702 -0.01868 0.12322 0.75767
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06330835 0.02664964 2.376 0.0193 *
total_sample -0.00002876 0.00004690 -0.613 0.5410
---
Signif. codes: 0 β***β 0.001 β**β 0.01 β*β 0.05 β.β 0.1 β β 1
Residual standard error: 0.2474 on 110 degrees of freedom
Multiple R-squared: 0.003407, Adjusted R-squared: -0.005653
F-statistic: 0.376 on 1 and 110 DF, p-value: 0.541
No meaningful relationship! (see code below.) However, big caveat here that we had to guess on some of the samples because many studies do not report how many subjects or meals were treated (e.g. they report how many restaurants or days were assigned to treatment and control but didnβt count how many people participated)
Thanks for the feedback on all my questions, Seth!