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Regression models are used to predict real values such as salary, spending, income. Simple linear regression is a model of regression which is used to identify the correlation between two variables and possibly predict the dependent variable by using the independent variable.

This will enable us to establish a relationship between two attributes such as Income and Spending and we can use what we know about the relationship to forecast unobserved values.

There should be two variables when it comes to simple linear regression. which are the dependent variable and the independent variable

Dependent Variable | Independent Variable |
---|---|

This is the value we want to forecast Value depends on another variable Usually denoted as \(x\) | This explains the other variable Independent of other values Usually denoted as \(y\) |

In simple linear regression, we use the equation,

$$y = \beta_{0} + \beta_{1}x$$

In which \(\beta_{0}\) is a constant (\(x\) intercept) and \(\beta_{1}\) is the coefficient or the slope of \(x\)

We call this a linear equation as it will represent a straight line if we were to plot this in a bidimensional plain.

So if you were to plot \$\$math-plot ,

The *y* intercept, in this case, is 4 that is the point where the line intercepts the vertical axis *y*. so $$\beta_{0}$$ is 4. and the slope, in this case, is 2 which we can obtain using $$\Delta y / \Delta x$$

But the data in the real world on average follows a linear pattern and they are not gonna be always in line with our equation. so there will be errors. and what are the errors? an error is at a given point, the difference between real value and the value presented by the equation

And what the regression algorithm going to do is minimize the errors and adjust the equation to create the line with the least possible errors.

So the linear regression model must include the error. After including the error, the model looks like this

$$y = \beta_0 + \beta_1 x + \varepsilon$$

Okay, now we have a basic idea about linear regression. let's go ahead and use it in R.

you can download the data set that I am going to use in this post from
Here .
Download the file as `salaryData.csv`

to a folder and set it as the working directory in your R editor

This data set contains a `Salary`

and `TotalExperience`

variables which we can assume to have some correlation between them.

below is a visualization of the data set.

Next step is to determine the dependent variable and the independent variable. we can intuitively choose the salary as the dependent variable. so `Salary`

will be the dependent variable and `TotalExperience`

will be the independent variable.

Now let's load the data set and split the data set into two sets (training set and test set). we will choose `.75`

as the ratio as we have 100 records in the data set. this is generally considered as a good ratio.

```
# Read the data set
data <- read.csv("salaryData.csv")
# Load the library
library(caTools)
set.seed(12345)
# Split the data set
sp <- sample.split(data$Salary, SplitRatio = 0.75)
trainingSet <- subset(data, sp == TRUE)
testSet <- subset(data, sp == FALSE)
```

We won't need to apply feature scaling this time because the package that we are going to use will take care of the values with different scales.

Okay, now we are going to create the regressor that will perform the actual regression. we have to specify the variables that we want to use and the data set to the `lm`

function which is in-built in R.

```
# Create the regressor
regressor <- lm(formula = Salary ~ TotalExperience, data = data)
```

And if you check the summary of regressor by typing `summary(regressor)`

in console, you can see the actual coefficient that the model is going to use. and it also shows the statistical significance of the independent variable. the more significant the variable, the more impact it has to the final result.

Now we are ready to predict the values. let's go ahead and create a prediction vector for our test data set.

```
testPredictions <- predict(regressor, newdata = testSet)
```

If now you evaluate the `testPredictions`

object, you will see all 25 predicted values for `Salary`

variable are in this vector.

Now we are ready to see some plots. we will have to use another library to visualize our data. We are going to use the `ggplot2`

package for this. Install the package if you have not installed it already.

We are going to plot the line for both the test set and the training set. so let's write a function to do this.

```
library(ggplot2)
# This function will plot the graph using ggplot2 library
visualize <- function(experience, salary) {
ggplot() +
ggtitle('Experience vs Salary') +
xlab('Years of Experience') +
ylab('Salary') +
geom_point(aes(x = experience, y = salary), colour= 'red') +
# We use the training set predictions in both cases as we used that set to create the regressor.
geom_line(aes(x = trainingSet$TotalExperience, y = predict(regressor, newdata = trainingSet)), color='blue')
}
# Plot the data for the training set
visualize(trainingSet$TotalExperience, trainingSet$Salary )
# Plot the data for the test set
visualize(testSet$TotalExperience, testSet$Salary )
```

this results in below graphs

for training data

for test data

With these plots, you can see the model has adjusted the equation to create the most error-free line possible.

Below is the complete script that we build in this article.

```
# Read the data set
data <- read.csv('salaryData.csv')
# Load the library
library(caTools)
set.seed(12345)
# Split the data set
sp <- sample.split(data$Salary, SplitRatio = 0.75)
trainingSet <- subset(data, sp == TRUE)
testSet <- subset(data, sp == FALSE)
# Create the regressor
regressor <- lm(formula = Salary ~ TotalExperience, data = data)
#Create the predictions
testPredictions <- predict(regressor, newdata = testSet)
library(ggplot2)
# This function will plot the graph using ggplot2 library
visualize <- function(experience, salary) {
ggplot() +
ggtitle('Experience vs Salary') +
xlab('Years of Experience') +
ylab('Salary') +
geom_point(aes(x = experience, y = salary), colour= 'red') +
# We use the training set predictions in both cases as we used that set to create the regressor.
geom_line(aes(x = trainingSet$TotalExperience, y = predict(regressor, newdata = trainingSet)), color='blue')
}
# Plot the data for the training set
visualize(trainingSet$TotalExperience, trainingSet$Salary )
# Plot the data for the test set
visualize(testSet$TotalExperience, testSet$Salary )
```