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Logistic Regression for Beginners

Understand the key components of logistic regression and develop a logistic regression model using SAS
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Rating: 4.3 out of 5 (10 ratings)
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1hr 38min of on-demand video
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Develop a logistic regression model using SAS
Know in detail about regression analysis
Explain logistic regression and its benefits
Understand about the key components of logistic regression
Know about the different methods of finding the probabilities
Learn how to interpret the modeling results and present it to others
Know how to interpret logistic regression analysis output produced by SAS

Requirements

  • Students or anyone taking this course should have some familiarity with SAS. There are no basic skills required to take this course.

Description

Logistic regression is also known as logit regression or logit model. This is used to find the probability of event success and event failure. Logistic regression determines the relationship between categorical dependent variable and one or more independent variables using a logistic function.

Logistic regression is used for predicting the probability of occurrence of an event by fitting the data to a logistic curve. Ordinary Least Squares on the other hand is an important computational problem that is used in applications when there is a need to use a linear mathematical model to measurements which are derived from the experiments. OLS takes various forms like Correlation, multiple regression, ANOVA and others. Logistic regression is most widely used in the field of medical science whereas OLS is mostly used in social sciences.

In this chapter we will see the comparison of logistic regression with OLS. Two methods are used to compare the results of both – Dropout study and High School and Beyond Study. There are many types of logistic models but this chapter will deal with the basic three types of logistic regression models – Binary, ordinal and nominal models.

Binary logistic regression is where a binary response variable is related to a set of explanatory variables which are discrete or continuous.

Multinomial logistic regression explains how a multinomial response depends on a set of explanatory variables. The polytomous response can be either or ordinal or nominal. There are few models which suits ordinal response like cumulative logit model, adjacent categories model and continuation ratios model. The other models can be used for both ordinal or nominal response.

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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