Learn about linear models for categorical variables, logistic transformation, logistic regression, likelihood, and confidence intervals for model parameters.
- [Instructor] How can we perform model fitting…when the response variables are categorical?…To answer this question, I'll follow the discussion…in David Kaplan's "Statistical Modeling."…An experiment with the smoking outcomes data set…that we used in chapter three.…So, I load packages and the data.…I have already written a function to plot nicely the data…and the fit predictions.…And to improve the strength of our conclusions…I have removed cases with age greater than 65,…none of whom is alive after 20 years.…
If I convert the smoking outcome to a binary number…it becomes possible to do ordinary least squares.…We'll try that first.…Although, we'll see that there is a much better way.…Here, I can use a Python trick.…Multiplying a Boolean by an integer returns an integer.…Let's fit a model that includes smoking status…and age as main terms.…
The stats models, ordinary least squares,…outcome, till dead, smoker plus age.…Data will be the smoking data frame,…and we can fit and assign to a variable.…Let's see what our plot does.…
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
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