Linear regression is a model used in statistics that details the relationship between a dependent variable and one or more independent variables.

Machine Learning, forecast modeling in various industries such as business, agriculture, weather, supply chain, among other ones.

The equation of a straight line is usually written as \(y = mx + c\).

• \(y\): Dependent variable
• \(x\): Independent variable
• \(m\): Slope of the line
• \(c\): Y-intercept

The equation of a simple linear regression line is:

\[ y = \beta_0 + \beta_1x \]

This plot shows the linear relationship between various data points

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

This example is like the previous one, but this one involves 2 groups of data points

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

R code that generates a plotly as an example for linear regression

library(plotly)
set.seed(123)
data = data.frame(
  x = 1:100,
  y = 2 * (1:100) + rnorm(100, mean = 0, sd = 20)
)

mod = lm(y ~ x, data = data)
plot_ly(data, x = ~x, y = ~y, type = "scatter", 
mode = "markers", name= "values") %>%
add_trace(x = ~x, y = fitted(mod), mode = "lines", 
name = "Regression Line")

Another example of linear regression with plotly