There has been growing interest in the introductory posts on the elementary topics in Machine Learning. So, I am writing on such topics in the coming posts starting from this one. This article is mostly self-contained however, it requires basic understanding of linear algebra, and calculus. Regression is the process of estimating the relationship of a dependent variable (Y) with one or more independent variables (Xi). It is used primarily for finding patterns in a given set of data samples and forecasting the value of a variable while given a set of values of other variables.

The STATA OMNIBUS: Regression and Modelling with STATA 4.5 (5 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Learn everything you need to know about linear regression, non-linear regression, regression modelling and STATA in one package. Learning and applying new statistical techniques can often be a daunting experience. "Easy Statistics" is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology. This course will focus on the concept of linear regression and non-linear regression.

Dear Dr. Granville, the Stata / SPSS implementation of non-linear regression is quite inflexible. The SPSS implementation is especially bad, allowing one to type only simple, non-recursive functions in a pop-up window. If you got a project about implementing a non-linear regression for a complex functional form, you would use R, Matlab or a similar programming language. Following the general vibe of responses, I changed the "Non-linear Regression / SPSS" field to "Limited" to avoid potential misinterpretations of the table. However, the truth is: the SPSS implementation of non-linear regression is unsatisfactory for most industry-level research. Lasso is available in SPSS only as part of categorical regression, which does not cover linear regression and generalized linear models.