linear Regression Raw R Algorithm
The linear Regression Raw R Algorithm is a powerful statistical method that aims to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. This algorithm is widely used in predictive modeling and forecasting, as it provides a simple yet effective way to understand the underlying trends and relationships within a dataset. The basic idea behind linear regression is to find the best-fitting straight line that captures the overall pattern of the data, which can then be used to make predictions about the dependent variable based on the values of the independent variables. This is accomplished by minimizing the sum of the squared differences between the actual data points and the corresponding points on the fitted line, a process known as least squares estimation.
The linear Regression Raw R Algorithm is implemented in the R programming language, which is a popular open-source language and environment for statistical computing and graphics. R provides a comprehensive set of functions and libraries for performing linear regression, including the "lm()" function, which allows users to easily fit a linear model to their data. In addition to the basic linear regression, R also supports more advanced techniques such as multiple linear regression, where multiple independent variables are used to predict the dependent variable, and polynomial regression, which involves fitting a polynomial equation to the data instead of a straight line. The flexibility and ease of use of the linear Regression Raw R Algorithm make it a popular choice among data analysts and researchers who want to quickly and effectively analyze and model relationships within their data.
ols<-function(y,x){
x<-as.matrix(x)
x<-cbind(intercept=1,x)
return(solve(t(x) %*% x) %*% t(x) %*% y)
}
ols(y=diamonds$price,x=diamonds %>% select(-price)) %>% print()
