Logistic Regression Model Fitting and Finding the Correlation, P-Value

Logistic Regression Model Fitting and Finding the Correlation, P-Value

Statical Model Fitting and Extract the Results from the Fitted Model Using Python’s Statsmodels Library with a Real-World Example. This article will explain a statistical modeling technique with an example. I will explain a logistic regression modeling for binary outcome variables here. That means the outcome variable can have only two values, 0 or 1.

Most data scientists who do not have strong statistics background may think of logistic regression as a machine learning model. That’s true. But it has been around for a long time in statistics.

Statistics is a very important part of data science and machine learning. Because it is essential for any type of exploratory data analysis or machine learning algorithm to learn the features of a dataset, how they relate to each other, how one feature affects the other features and the overall output.

Luckily python has this amazing library that is ‘statsmodels’ library. This library has great functionalities to understand the dataset and also we can use this library to make predictions. Statsmodels library already has models in-built that can be fitted to the data to find the correlation between the features, learn the coefficients, p-value, test-statistic, standard error, and confidence interval.

This article will explain a statistical modeling technique with an example. I will explain a logistic regression modeling for binary outcome variables here. That means the outcome variable can have only two values, 0 or 1.

We will also analyze:

1. the correlation amongst the predictor variables (the input variables that will be used to predict the outcome variable),

2. how to extract useful information from the model results,

3. the visualization techniques to better present and understand the data and

4. the prediction of the outcome. I am assuming that you have the basic knowledge of statistics and python.

The sequence of discussion:

  1. A basic logistic regression model fitting with one variable
  2. Understanding odds and log-odds with examples
  3. Logistic regression model fitting with two variables
  4. Logistic regression model fitting with three variables
  5. Visualization of the fitted model
  6. Prediction

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