The material is an interactive application developed in shiny to demonstrate the effect of different factors on the logistic regression model and empirical logit plot.This material is great for using in class to demonstrate the choice of parameters on the shape of the logit curve and empirical logit plot.
It uses simulations and real data to explore simple logistic regression models and methods for examining the assumptions of the model.
Type of Material:
Animation
It is a web page with background context , visualizations and interactive components that are accessible via different tabs.
Recommended Uses:
This is a great companion site for demonstrating logistic regression. Under the explore tab there is a short assessment quiz. Also it could be used in class, homework, individual, team, lecture, self-paced,etc.
Technical Requirements:
Javascript enabled browser
Identify Major Learning Goals:
The purpose of the application is to provide a deeper understanding of a single regressor logistic regression model by manipulating various parameters such as intercept, slope, sample size. There is also an option to vary the confidence interval level and select to display pearson or deviance residuals.
Target Student Population:
High School, College Lower Division, College Upper Division, Graduate School
Prerequisite Knowledge or Skills:
Basic knowledge of model fitting, binomial response and logistic regression. Some of which is provided under prerequisites tab.
Content Quality
Rating:
Strengths:
Interactive learning of how different coefficients affect the shape of the logistic curve.
There is an assessment quiz under the “Game tab” which can be used as a formative assessment to check for understanding.
Concerns:
A minor concern is that it only tackles a single regressor case. It would have been great to include context on when a logistic regression model is used in order to make the students see it as a decision making tool.
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
This material can be used in class for demonstrating the concept by the instructor or by students to explore and assess.
Concerns:
Before interacting with the material, the students need to have a background on model fitting, binomial distribution and a brief introduction to logistic regression.
Identifying the objectives could take the students to a better understanding of the use of logistic regression for prediction and classification problems.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
The instructions are clear. There is an assessment as well under the Game tab to check for understanding.
Creative Commons:
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