Hi,

I am used to analyse R regression coefficients and I am a little bit confused about how to do it in dataiku. For instance on the Iris dataset, If I fit a regression on the iris dataset to explain sepal length with the Species and the Petal length I have :

```Call:
lm(formula = iris\$Sepal.Length ~ iris\$Petal.Length + iris\$Species)

Residuals:
Min       1Q   Median       3Q      Max
-0.75310 -0.23142 -0.00081  0.23085  1.03100

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)             3.68353    0.10610  34.719  < 2e-16 ***
iris\$Petal.Length       0.90456    0.06479  13.962  < 2e-16 ***
iris\$Speciesversicolor -1.60097    0.19347  -8.275 7.37e-14 ***
iris\$Speciesvirginica  -2.11767    0.27346  -7.744 1.48e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.338 on 146 degrees of freedom
Multiple R-squared:  0.8367,    Adjusted R-squared:  0.8334
F-statistic: 249.4 on 3 and 146 DF,  p-value: < 2.2e-16```

The two regression coefficients iris\$Speciesversicolor, iris\$Speciesvirginica are to compared with the Species taken as reference (Setosa). Meaning, that  iris\$Speciesvirginica  is the difference of sepal length in mean between the species virginica and setosa.

In dataiku, I have three coefficient and I don't know what is the reference. Besides, none of my coefficients are significative in dataiku whereas there are all significative in R :

```species = Iris-virginica   ☆☆☆  9.01e-21.3485   0.3129
species = Iris-setosa  ☆☆☆   8.16e-2-1.4032-       0.3081
petal_l                          ☆☆☆     4.02e-10.2486      0.2450
species = Iris-versicolor ☆☆☆ 4.57e-1-0.1091-0.0233
Intercept   5.8531```

Could you explain why?

edited