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Introduction to Regression with statsmodels in Python

Intermediate
4.4+
40 reviews
Updated 12/2024
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
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PythonProbability & Statistics4 hours14 videos53 exercises4,150 XP40,737Statement of Accomplishment

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Course Description

Use Python statsmodels For Linear and Logistic Regression

Linear regression and logistic regression are two of the most widely used statistical models. They act like master keys, unlocking the secrets hidden in your data. In this course, you’ll gain the skills to fit simple linear and logistic regressions.

Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more.

Discover How to Make Predictions and Assess Model Fit

You’ll start this 4-hour course by learning what regression is and how linear and logistic regression differ, learning how to apply both. Next, you’ll learn how to use linear regression models to make predictions on data while also understanding model objects.

As you progress, you’ll learn how to assess the fit of your model, and how to know how well your linear regression model fits. Finally, you’ll dig deeper into logistic regression models to make predictions on real data.

Learn the Basics of Python Regression Analysis

By the end of this course, you’ll know how to make predictions from your data, quantify model performance, and diagnose problems with model fit. You’ll understand how to use Python statsmodels for regression analysis and be able to apply the skills to real-life data sets.

Prerequisites

Introduction to Data Visualization with SeabornIntroduction to Statistics in Python
1

Simple Linear Regression Modeling

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2

Predictions and model objects

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3

Assessing model fit

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4

Simple Logistic Regression Modeling

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Introduction to Regression with statsmodels in Python
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*4.4
from 40 reviews
68%
18%
5%
8%
3%
  • patrick l.
    2 months

    Large experience datasets

  • Urich K.
    6 months

    Very good course

  • Sue D.
    7 months

    Interesting course and awesome instructor!

  • Andreas P.
    10 months

    A great course with a clear speaker and some good examples. One issue: in part 2, it became a tad more complicated, it would be better if there was more time or more examples to explain prediction model elements more clearly. I particularly enjoyed part 4, with the measures of the confusion matrix.

  • Ana U.
    11 months

    Simple regression analysis in Python is an extraordinary course. The instructor is wonderful. The exercises are challenging and created to give the student a sequence of set-by-step learning process. I learned and enjoy this course so much that I already register for the following course.

"Large experience datasets"

patrick l.

"Very good course"

Urich K.

"Interesting course and awesome instructor!"

Sue D.

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