library(ggplot2)
library(plotly)
library(dplyr)
data(airquality)
data(cars)
ggplotter <- ggplot(airquality, aes(x = Temp, y = Ozone)) + geom_point() + 
  geom_smooth(method = "lm") + 
  coord_cartesian(ylim = c(0,100))
ggplotter2 <- ggplot(cars, aes(x = speed, y = dist)) + geom_point() + 
  geom_smooth(method = "lm") + 
  coord_cartesian(ylim = c(0,100))
airquality_na <- na.omit(airquality)
line <- lm(Ozone ~ Temp, data = airquality_na)
line2 <- lm(dist ~ speed, data = cars)
x = airquality_na$Temp; y = airquality_na$Ozone
plotter = plot_ly(data = airquality_na,x= ~Temp,y = ~Ozone, type = "scatter"
,mode = "markers", width = 400, height = 400,name = "Data") %>%
  add_lines(x = x, y = fitted(line), name = "Regression") 

This is the linear regression equation for the airquality lines. The y value for this represents the Ozone value. The first decimal value represent the value of “Ozone” when the temp value is at 0. Finally, the second decimal value is the slope or ozone increase per one temperature value increase. \[ \hat{y} = -147.646 + 2.439 \cdot \]

This is the linear regression equation for the cars line. The y value for this equation represents the distance for cars traveling. The first decimal value represents the Distance value when the speed is zero. The second decimal value is the distance increase for each single speed increase. \[ \hat{y} = -17.579 + 3.932 \cdot \]

I used documentation from:
plot_ly,
slidesn
and doc2latex to understand how to write math equations in this format.