Logistic Regression & Supervised Learning using SAS Stat
What you'll learn
- Know in detail about regression analysis
- Explain logistic regression and its benefits
- Understand about the key components of logistic regression
- Know how to interpret logistic regression analysis output produced by SAS
- Students or anyone taking this course should have some familiarity with SAS. There are no basic skills required to take this course.
The SAS logistic regression is mainly used to predict the result of the categorical dependent variable based upon one or more dependent and independent variables for using multiple regression. The logistic regression is primarily based upon the maximum likelihood(ML) estimation algorithm, in which coefficients maximize the probability for different versions and iterations to choose the values for regression parameters and estimate the data ratio to interpret each other.
It is one of the SAS models and is mainly used for data beginners; the logistic model will mostly share the common feature. The general class of the linear models will mean the response variable for assuming the other explanatory variables. It has other means like pi, and implicit data depends on the response behaviour variable to be fixed. This logistic procedure will fit the linear regression models for response data by the maximum method to perform logistic conditions and regression. It also enables the categorization of the procedure and specific categorical variables, also known as the classification of class variables. It is mainly followed continuously with the variable on explanatory procedure effects. It supports other complex models, and data interactions will use the nested data terms with the GLM procedure.
The logistics regression model is the SAS basic model for predicting the dependent variable’s definite results based on one or more continuous. It followed with the dependent and independent variables for data regression analysis to calculate the factors for both promoted and non-promoted in data performance ratings.
Who this course is for:
- Researchers, Forensic statisticians, Data Miners, Environmental Scientists, Epidemiologists
- Anyone who is interested in modeling data and estimate the probabilities of given outcomes
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