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R, Python, and SAS: Getting Started with Linear Regression

Consider the linear regression model, $$ y_i=f_i(\boldsymbol{x}|\boldsymbol{\beta})+\varepsilon_i, $$ where $y_i$ is the response or the dependent variable at the $i$th case, $i=1,\cdots, N$ and the predictor or the independent variable is the $\boldsymbol{x}$ term defined in the mean function $f_i(\boldsymbol{x}|\boldsymbol{\beta})$. For simplicity, consider the following simple linear regression (SLR) model, $$ y_i=\beta_0+\beta_1x_i+\varepsilon_i. $$ To obtain the (best) estimate of $\beta_0$ and $\beta_1$, we solve for the least residual sum of squares (RSS) given by, $$ S=\sum_{i=1}^{n}\varepsilon_i^2=\sum_{i=1}^{n}(y_i-\beta_0-\beta_1x_i)^2. $$ Now suppose we want to fit the model to the following data, Average Heights and Weights for American Women , where weight is the response and height is the predictor. The data is available in R by default.

SAS®: Getting Started with PROC IML

Another powerful procedure of SAS, my favorite one, that I would like to share is the PROC IML (Interactive Matrix Language). This procedure treats all objects as a matrix, and is very useful for doing scientific computations involving vectors and matrices. To get started, we are going to demonstrate and discuss the following: Creating and Shaping Matrices; Matrix Query; Subscripts; Descriptive Statistics; Set Operations; Probability Functions and Subroutine; Linear Algebra; Reading and Creating Data; Above outline is based on the IML tip sheet (see Reference 1). So to begin on the first bullet, consider the following code:

New Toy: SAS® University Edition

So I started using SAS® University Edition which is a FREE version of SAS® software. Again it's FREE, and that's the main reason why I want to relearn the language. The software was announced on March 24, 2014 and the download went available on May of that year . And for that, I salute Dr. Jim Goodnight . At least we can learn SAS® without paying for the expensive price tag, especially for single user like me. The software requires a virtual machine, where it runs on top of that; and a 64-bit processor. To install, just follow the instruction in this video . Although the installation in the video is done in Windows, it also works on Mac. Below is the screenshot of my SAS® Studio running on Safari.