Simple linear regression

> # Input data
> head(my_df)
               Y            X
[1,] -0.39643897  0.014797410
[2,] -1.11042940 -1.634525956
[3,] -0.02086076 -0.340575102
[4,]  1.90382401  0.117504381
[5,]  0.62557591  1.042435648
[6,] -1.40269719 -0.006455178
> dimmy_df
dim                    dim.data.frame         dim<-                  dimnames               dimnames.data.frame    dimnames<-             dimnames<-.data.frame
> dim(my_df)
[1] 50  2

######

> lm(data=my_df, " Y ~ X ")

Call:
lm(formula = " Y ~ X ", data = my_df)

Coefficients:
(Intercept)            X
 -1.076e-17    5.600e-01

> model = lm(data=my_df, " Y ~ X ")
> summary(model)

Call:
lm(formula = " Y ~ X ", data = my_df)

Residuals:
     Min       1Q   Median       3Q      Max
-1.65443 -0.57053 -0.06958  0.47720  1.83802

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.076e-17  1.184e-01   0.000        1
X            5.600e-01  1.196e-01   4.683 2.35e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8371 on 48 degrees of freedom
Multiple R-squared:  0.3136,    Adjusted R-squared:  0.2993
F-statistic: 21.93 on 1 and 48 DF,  p-value: 2.352e-05