Logistics Analyst Salary – The Perfect Career For You?
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This video describes how to do Logistic Regression in R, step-by-step. We start by importing a dataset and cleaning it up, then we perform logistic regression on a very simple model, followed by a fancy model. Lastly we draw a graph of the predicted probabilities that came from the Logistic Regression.
The code that I use in this video can be found on the StatQuest GitHub:
https://github.com/StatQuest/logistic_regression_demo/blob/master/logistic_regression_demo.R
For more details on what’s going on, check out the following StatQuests:
For a general overview of Logistic Regression:
The odds and log(odds), clearly explained:
The odds ratio and log(odds ratio), clearly explained:
Logistic Regression, Details Part 1, Coefficients:
Logistic Regression, Details Part 2, Fitting a line with Maximum Likelihood:
Logistic Regression Details Part 3, R-squared and its p-value:
Saturated Models and Deviance Statistics, Clearly Explained:
Deviance Residuals, Clearly Explained:
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0:00 Awesome song and introduction
0:29 Load and format data
3:54 Dealing with missing data
5:03 Verifying that the data is not imbalanced
6:44 Logistic regression with one independent variable
12:48 Logistic regression with many independent variables
15:13 Graphing the predicted probabilities
#statquest #logistic
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Learn how to use R to fit a model to a binary (yes/no) response variable, regardless of whether you have raw data or proportions. If this vid helps you, please help me a tiny bit by mashing that ‘like’ button. For more #rstats joy, crush that ‘subscribe’ button!
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