This is probably more a statistical question rather than an R question, however I want to know how this lm() anaysis comes out with a significant adjusted p-value (p=0.008) when the St Err on the change in IGF2 (-0.04ng/ml) for every Kg increase in weight is huge (0.45ng/ml). The confidence interval of the effect size is therefore massive (-0.9-0.8).

I think I must be reading the output wrong.

Thanks in advance for any help

Kate

```
Call:
lm(formula = Cohort1$V1_IGF2_Result ~ Cohort1$SCREEN_Weight +
Cohort1$Sex + Cohort1$ETH)
Residuals:
Min 1Q Median 3Q Max
-451.62 -95.15 0.49 88.59 394.98
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 806.19536 81.65754 9.873 <2e-16 ***
Cohort1$SCREEN_Weight -0.04404 0.45061 -0.098 0.9222
Cohort1$SexM -44.49234 24.63328 -1.806 0.0723 .
Cohort1$ETHE 31.00856 74.11799 0.418 0.6761
Cohort1$ETHM -15.78481 80.27579 -0.197 0.8443
Cohort1$ETHO -29.85577 75.71341 -0.394 0.6937
Cohort1$ETHP -47.73104 77.43752 -0.616 0.5383
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 144.6 on 216 degrees of freedom
Multiple R-squared: 0.07641, Adjusted R-squared: 0.05076
F-statistic: 2.978 on 6 and 216 DF, p-value: 0.008154
```