{"id":15464,"date":"2018-05-14T05:52:57","date_gmt":"2018-05-14T05:52:57","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=15464"},"modified":"2021-12-03T10:34:59","modified_gmt":"2021-12-03T05:04:59","slug":"robust-regression-in-sas","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/robust-regression-in-sas\/","title":{"rendered":"Robust Regression in SAS\/STAT – 3 Best Procedures"},"content":{"rendered":"

1. Objective<\/h2>\n

In the last article, we discussed SAS Power and Sample Size Analysis<\/a><\/strong>. Today, we will be looking at another type of analysis, called Robust\u00a0Regression in SAS\/STAT and how can we use SAS\/STAT robust regression. Our focus here will be to understand the SAS\/STAT\u00a0robust regression Procedures: PROC QUANTREG, PROC QUANTSELECT, and PROC ROBUSTREG with example\u00a0& syntax.
\nSo, let’s begin with\u00a0Robust Regression in SAS\/STAT.<\/p>\n

\"Robust<\/a>

Robust Regression in SAS\/STAT – 3 Best Procedures<\/p><\/div>\n

2. What is Robust Regression in SAS\/STAT?<\/h2>\n

Robust regression is designed to overcome the limitations, which are arises from traditional parametric and non-parametric methods. Robust regression in SAS\/STAT is a form of regression analysis<\/strong><\/a>.\u00a0It is also similar to least squares regression, is a technique used for those datasets in which the variables<\/strong><\/a> and the features exhibit a non-linear trajectory and the assumptions that form the basis of the dataset are likely to change in future.
\nLet’s discuss Important SAS\/STAT Longitudinal Data Analysis Procedures<\/a><\/strong>
\nRobust regression in SAS\/STAT is a statistical procedure used for modeling a regressor in the presence of an outlier in the dataset<\/strong> <\/a>and can also support any anomalous detection. It can be used along with many machine learning<\/strong> <\/a>and computing approaches such as :<\/p>\n

    \n
  1. Linear regression<\/li>\n
  2. Markovian logic<\/li>\n
  3. Fuzzy system<\/li>\n
  4. Expectation maximization algorithm.<\/li>\n<\/ol>\n
    \"Robust<\/a>

    SAS\/STAT Robust Regression – Sample<\/p><\/div>\n

    3. SAS\/STAT\u00a0Robust Regression Procedures<\/h2>\n

    Following procedures to compute robust regression in SAS\/STAT of a sample data. Let us explore it.<\/p>\n

    a. PROC ROBUSTREG<\/h3>\n

    SAS ROBUSTREG procedure in SAS\/STAT is used to detect outliers. Outliers are basically observations that lie very far or at an abnormal distance from our population. In the presence of these outliers, stable results are provided by limiting the influence of these outliers.
    \nThe robust regression\u00a0in SAS\/STAT cater to three different classes of problems:<\/p>\n