Author : Amritha Varma
Locally Estimated Scatterplot Smoothing (LOESS) is a regression tool which help us to create a smooth line between the scatter plot. It helps us to show the relation between the variables and trends of variables. It is a non parametric regression method which combines multiple regression in K-nearest neighbor. Non parametric regression finds a curve without assuming data. This smoothing function captures general patterns and it makes assumptions about relationship among variables. The result of LOESS is a line moving through central tendency. It is mainly used to show the relationship between two variables with large data sets.
What is regression?
What is smoothing?
WHAT IS REGRESSION:-
Regression is a mathematical function which show the relationship between one dependent variable and one more variable. The obtained function is called regression equation.
WHAT IS SMOOTHING:-
Smoothing is a technique to group variables with similar expectations and fit a suitable curve. It helps to decrease the volatility in data series. Therefore trend can be observed clearly.
Fitting a line to a scatter plot where noisy data values with your ability to see a line of best fit.
Linear regression where least squares fitting does not create a line of good fit
Data explorations in social science.
Easy to use
Complex in nature
Difficult for explaining result
No on hand formula so it is difficult to transport results.
Here the scatterplot will be displayed on the basis of car dataset with box plots in the margins and non-parametric regression smooth.
x <- mtcars$wt y <- mtcars$mpg plot(x, y, main = "Main title", xlab = "X axis title", ylab = "Y axis title", pch = 19, frame = FALSE) lines(lowess(x, y), col = "green")