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using DataFrames # Appelle de la librairie DataFrames
using GLM # Appelle de la librairie linear model and generalized linear model
data=DataFrame(X=[1,2,3],Y=[2,4,7]) # On prend trois points X et Y.
OLS = glm(Y~X,data,Normal(),IdentityLink()) # On calcule la droite linéaire. 
# data.Normal est à la résolution au sens des moindre carré ou des équivalents.
# IdentityLink correspond a une loi Normal sans transformation.

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DataFrameRegressionModel{GeneralizedLinearModel{GlmResp{Array{Float64,1},Normal,IdentityLink},DensePredChol{Float64,Cholesky{Float64}}},Float64}:

Coefficients:
Estimate Std.Error  z value Pr(>|z|)
(Intercept)  -0.666667   0.62361 -1.06904   0.2850
X                  2.5  0.288675  8.66025   <1e-17

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data = data.frame(X=c(1,2,3), Y=c(2,4,7))
summary(glm(Y~X, data=data, normal()))

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Call:
glm(formula = Y ~ X, data = data)

Deviance Residuals: 
      1        2        3  
 0.1667  -0.3333   0.1667  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.6667     0.6236  -1.069   0.4788  
X             2.5000     0.2887   8.660   0.0732 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.1666667)

    Null deviance: 12.66667  on 2  degrees of freedom
Residual deviance:  0.16667  on 1  degrees of freedom
AIC: 5.8425

Number of Fisher Scoring iterations: 2