The simplest linear regression in R

This article is about simple calculation of parameters of linear regression in R script.

If you are interested in linear regression in Excel, click here.

Let´s start with data uploading. In this example we will observe the dependency of sales on number of adds.

number_of_adds = c(10,15,22,8,14,20,32,27)
number_of_sold_products = c(17,27,37,13,25,34,60,49)

Create a data frame from vectors:

mydata=data.frame(number_of_adds,number_of_sold_products)

Create a linear model from data frame.

mymodel = lm(number_of_sold_products ~ number_of_adds, data = mydata)

Now you can show the model and its parameters:

print(mymodel)

Coefficients:
(Intercept) number_of_adds 
-2.377 1.899

You can also draw it:

plot(mydata)

What does the results mean?

• The dots in charts makes quite straight increasing line. This means there is positive linear dependency – the more adds, the bigger sales. 
• The parameter -2,377 says in which point this line crosses the y axis.
• The parameter 1,899 describes the steepness of line – the higher, the bigger impact of adds on sales.