Logistic Regression for Beginners

Name: Logistic Regression for Beginners
Rating: 4.3 (16 reviews)

Understand the key components of logistic regression and develop a logistic regression model using SAS

Created byEDUCBA Bridging the Gap

Last updated 11/2023

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What you'll learn

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

Course content

5 sections • 15 lectures • 1h 38m total length

Introduction1:52
Explore the fundamentals of regression and the reasons to use logistic regression over linear regression, then review key concepts, approaches (binning, continuous, dummy), and SAS proc logistic with goodness-of-fit tests.

What is Regression Part 17:24
Explore regression as a statistical method that explains a dependent variable y using independent variables x and an error term epsilon, and supports prediction with linear regression and least squares.
What is Regression Part 28:29
Explore regression analysis, from specifying dependent and independent variables and estimating unknown parameters with least squares. Learn about model assumptions, linearity, and how to assess fit and predictions.
What is Regression Part 311:23
Explore key regression concepts—regression equation, dependent and independent variables, coefficients, p values, r squared, adjusted r squared, and residuals—to build and assess predictive models.

Different Methods of Predicting Probabilities Part 12:39
Explore methods for predicting probabilities, including decision trees, survival analysis, OLS, GLM, multinomial and logistic regression, and learn how decision trees visualize tests, outcomes, and conditional probabilities.
Different Methods of Predicting Probabilities Part 29:11
Explore how survival analysis models time to event and how linear and generalized linear models relate to predicting probabilities, including logistic regression and logit in the exponential family.

What is Logistic Regression4:29
Logistic regression predicts a dichotomous outcome by estimating the probability of an event within a generalized linear model, unlike ordinary least squares.
Why Logistic Regression and Not OLS2:55
Explore why logistic regression outperforms OLS for binary outcomes. Explain how probabilities stay between 0 and 1 and why a sigmoidal relationship matters.
Modeling Key Concepts8:54
Explore key modeling concepts for logistic regression, including observation and performance periods, snapshot points, and rolling vintages, plus data cleansing, outlier treatment, and validation with training and holdout samples.
Logistic Regression Key Concepts Part 17:28
Learn logistic regression fundamentals: maximum likelihood estimation, the regression equation, and odds ratios; and how to convert odds to probability for a dichotomous outcome.
Logistic Regression Key Concepts Part 23:10
Learn how to convert probability to odds using p to 1-p, compare two binary cases with odds ratios in logistic regression.
Binning Approach and Other Approaches10:55
Review the binning, continuous variable, and dummy variable approaches to logistic regression, using weight of evidence and information value, with automatic and manual binning and emphasis on monotonicity.

SAS Methodology Part 110:14
Explore SAS proc logistic and the three variable selection methods: forward, backward, and stepwise, for building logistic regression models from many predictors. Examine multicollinearity, odds ratios, and Wald chi-square goodness-of-fit.
SAS Methodology Part 24:37
Learn sas logistic methodology including concordant, discordant, and tied pairs, predicted probabilities, vifs for multicollinearity, and interpreting model fit, odds ratios, and confidence intervals.
Model Fit and Thank You4:44
Assess logistic regression goodness of fit using global and local measures, including the Hosmer-lemeshow test, chi square, deviance, and roc curves, and validate with outside data or time-based splits.

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.

Logistic Regression for Beginners

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 2min

Regression Analysis3 lectures • 27min

Predicting Probabilities2 lectures • 12min

Logistics Regression6 lectures • 38min

SAS Methodology3 lectures • 20min

Requirements

Description

Who this course is for: