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Introduction to Regression with statsmodels in Python

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in Python.

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Description

In this course, you will :

  • Learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to use model coefficients to describe the relationship between the response and explanatory variables.
  • Learn how to use linear regression models to forecast Taiwanese house prices and Facebook ad clicks.
  • Learn how to ask questions of your model in order to determine fit.
  • You'll learn how to quantify how well a linear regression model fits, diagnose model problems with visualisations, and understand the leverage and influence of each observation in creating the model.
  • By the end of this course, you'll understand how to make predictions from data, quantify model performance, and diagnose model fit issues.

Syllabus :

  • Simple Linear Regression Modeling
  • Predictions and model objects
  • Assessing model fit
  • Simple Logistic Regression Modeling

Course Features

Enrollment options

Standard

  • 7 - days Free Trial
  • Unlimited access to 350+ Courses
  • Unlimited access to 50+ Skill tracks
  • Practice Challenges
  • Certificate on completion
  • Peer Support
  • Live coding
  • Skill Assessments
  • 25ドル/month - Annual Plan (13% saving)
  • 29ドル/month - Monthly Plan

Premium

  • 7 - days Free Trial
  • Unlimited access to 350+ Courses
  • Unlimited access to 80+ Projects
  • Unlimited access to 50+ Skill tracks
  • Practice Challenges
  • Certificate on completion
  • Peer Support
  • Live coding
  • Skill Assessments
  • Priority Support
  • 33ドル/month - Annual Plan (32% saving)
  • 49ドル/month - Monthly Plan
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