Statistical Computing and Graphics in R
Regression is to build a function of independent variables (also known as predictors, regressors, explanatory variables, and features) to predict a dependent variable (also called a response, target, and regressand). Here we will focus on performing linear regression in R …
Here, we will consider the data mtcars to get descriptive statistics in R. You can use a dataset of your own choice. Getting Dataset Information in R Before performing any descriptive or inferential statistics, it is better to get some …
Many generic functions are available for the computation of regression coefficients, for example, testing the coefficients, computing the residuals, prediction values, etc. Therefore, a good grasp of the lm() function is necessary. Suppose, we have performed the regression analysis using …
The linear regression model is typically estimated by the ordinary least squares (OLS) technique. The model in general form is $$Y_i=x’_i\beta + \varepsilon, \quad\quad i=1,2,\cdots,n$$ In matrix notation $$y=X\beta + \varepsilon,$$ where $y$ is a vector of order $n\times 1$ …
