### Import Data

```
# install.packages("sp")
library(sp)
data(meuse)
```

### Build a Model using Simple Linear Regression

Linear regression is one of the most traditional way of examining the relationships among predictors and variables. As we discussed in a previous post about the general idea of modeling and machine learning, we may have the purpose of inference the relationships among variables.

**Goal**: examine the relationship between the topsoil lead concentration (**lead** column, as y-axis) and the topsoil cadmium concentration (**cadmium** column, as x-axis).

We use the `lm`

function over here:

```
m1<-lm(lead~cadmium,data = meuse)
m1
```

```
##
## Call:
## lm(formula = lead ~ cadmium, data = meuse)
##
## Coefficients:
## (Intercept) cadmium
## 71.44 25.24
```

`lead = 25.24 * cadmium + 71.44`

The model above shows that, on average, when cadmium increases 1 unit, lead increases 25.24 units.

Please refer to our later posts about the visualization and analysis of the model.