Hi,

I was comparing Jamovi with R and I find a striking difference of the intercept when I do a linear model,

Say I have:

"Picture",30

"Picture",35

"Picture",45

"Picture",40

"Picture",50

"Picture",35

"Picture",55

"Picture",25

"Picture",30

"Picture",45

"Picture",40

"Picture",50

"Real Spider",40

"Real Spider",35

"Real Spider",50

"Real Spider",55

"Real Spider",65

"Real Spider",55

"Real Spider",50

"Real Spider",35

"Real Spider",30

"Real Spider",50

"Real Spider",60

"Real Spider",39

Jamovi is giving me:

Predictor Estimate SE t p

Intercept 43.50 2.08 20.90 < .001

Group:

Real Spider – Picture 7.00 4.16 1.68 0.107

But R,

With:

m1 <- lm(Anxiety ~ Group, data=spiderLong)

summary(m1)

R version 3.5.0 (2018-04-23) -- "Joy in Playing"

Copyright (C) 2018 The R Foundation for Statistical Computing

Platform: x86_64-w64-mingw32/x64 (64-bit)

> m1 <- lm(Anxiety ~ Group, data=spiderLong)

> summary(m1)

Call:

lm(formula = Anxiety ~ Group, data = spiderLong)

Residuals:

Min 1Q Median 3Q Max

-17.0 -8.5 1.5 8.0 18.0

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 40.000 2.944 13.587 3.53e-12 ***

GroupReal Spider 7.000 4.163 1.681 0.107

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 10.2 on 22 degrees of freedom

Multiple R-squared: 0.1139, Adjusted R-squared: 0.07359

F-statistic: 2.827 on 1 and 22 DF, p-value: 0.1068

Why the intercept is giving 40 in one case and 43.5 in the other??

Thanks for any hint!

José