Simple linear regression

Creating a Simple Linear Regression Model In R

We are going to create a simple linear regression model. lets getting started by loadig the required library

library(datarium)
library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.6     v purrr   0.3.4
## v tibble  3.1.7     v dplyr   1.0.9
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)

We will be using the marketing data set from datarium library. lets Now load our data set. After loading our data set we use the head() to get a view of it.

data<-marketing
head(marketing,5)

##   youtube facebook newspaper sales
## 1  276.12    45.36     83.04 26.52
## 2   53.40    47.16     54.12 12.48
## 3   20.64    55.08     83.16 11.16
## 4  181.80    49.56     70.20 22.20
## 5  216.96    12.96     70.08 15.48

From the data we loaded we will use the linear model to the relationship sales and youtube. We now try and visualize the data set we will be using to create our model usig the ggplot2() package

ggplot(marketing, aes(x = youtube, y = sales)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Graph plotted shows the relationship between the sales and the youtube linear regression is that the relationship between the outcome and predictor variables is linear and additive.

Now lets check the correlation between our variables

cor(marketing$sales, marketing$youtube)

## [1] 0.7822244

Now lets create our linear regression model

model <- lm(sales ~ youtube, data = marketing)
model

## 
## Call:
## lm(formula = sales ~ youtube, data = marketing)
## 
## Coefficients:
## (Intercept)      youtube  
##     8.43911      0.04754

The simple linear regression tries to find the best line to predict sales on the basis of youtube advertising budget. linear model equation can be written as follow: sales = b0 + b1 * youtube and R function lm() can be used to determine the beta coefficients of the linear model: The results show the intercept and the beta coefficient for the youtube variable, the estimated regression line equation can be written as follow: sales = 8.44 + 0.048*youtube

#Lets add a regression line then plot

ggplot(marketing, aes(x=youtube,y=sales)) +
  geom_point() +
  stat_smooth(method = lm)

## `geom_smooth()` using formula 'y ~ x'

#Lets assess our model

summary(model)

## 
## Call:
## lm(formula = sales ~ youtube, data = marketing)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.0632  -2.3454  -0.2295   2.4805   8.6548 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.439112   0.549412   15.36   <2e-16 ***
## youtube     0.047537   0.002691   17.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.91 on 198 degrees of freedom
## Multiple R-squared:  0.6119, Adjusted R-squared:  0.6099 
## F-statistic: 312.1 on 1 and 198 DF,  p-value: < 2.2e-16

Congrats !!!! You have justed created your first linear regression model in R

Simple linear regression

Kelvin Nyongesa

2022-07-02

Creating a Simple Linear Regression Model In R