```
rm(list = ls()) #remove ~ everything in the working environment
myX<-1:10
myY<-jitter(1:10)
adjust<-lm(myY ~ myX) # Use adjust<-lm(myY ~ 0 + myX) to force the intercept at 0
plot(myX,myY,abline(adjust))
```

The first line is to clear the working environment (e.g. delete variables) and I recommend to use it everytime you start a new script. The next two lines are used to generate 2 vectors (*myX* and *myY*). Note that I use `<-`

and not `=`

to assign a value to a variable (= works nicely here but it is best not to use it. See here for some explanations). *myX* is a vector containing 10 integer values (from 1 to 10), *myY* is *myX* with some noise (using the function jitter). We can have a look at these two vectors just by typing their names.

`myX`

`## [1] 1 2 3 4 5 6 7 8 9 10`

`myY`

```
## [1] 0.9119364 1.8619898 2.8056814 4.1016091 4.8022902 5.8452231 7.1772783 7.9499693
## [9] 9.1782965 9.9774281
```

Because It is easy to get the estimated parameters for the Slope (0.9843) and the Intercept (0.1337). Just type:

`adjust`

```
##
## Call:
## lm(formula = myY ~ myX)
##
## Coefficients:
## (Intercept) myX
## -0.1653 1.0230
```

The Coefficient of determination (which can be used to asses the goodness of fit for a linear model) is:

`summary(adjust)[8]`

```
## $r.squared
## [1] 0.9983183
```

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