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Using Statsmodels For Regression Data 88e Economic Models Textbook

Leo Migdal
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using statsmodels for regression data 88e economic models textbook

Data 88E: Economic Models is a course offered at UC Berkeley as part of the Data Science Connector Course series. It’s designed to bridge the gap between data science and economics by using real-world datasets and Python programming to illustrate key economic concepts. The course is intended for students from both data science and economics backgrounds, giving them a chance to apply computational tools to economic models. Students work with Python in Jupyter Notebooks to explore economic concepts such as supply and demand, market equilibrium, utility, game theory, and more. The course emphasizes how economic decisions are influenced by real-world data and policy interventions, making it a practical intersection of economics and data science. As a connector course to the main Data 8 course, Data 88E labs mostly use a specific Python package developed at UC Berkeley for teaching.

This package is known as datascience tables. Furthermore the notebooks also make extensive use of a Python package for automatic grading called otter-grader. Both packages are available from pypi using pip install XXX . I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understanding what’s happening under the hood. That’s where statsmodels shines. Unlike scikit-learn, which optimizes for prediction, statsmodels gives you the statistical framework to understand relationships in your data.

Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter. Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS). Think of it as the statistical counterpart to scikit-learn. Where scikit-learn focuses on prediction accuracy, statsmodels focuses on inference: understanding which variables matter, quantifying uncertainty, and validating assumptions. The library gives you detailed statistical output including p-values, confidence intervals, and diagnostic tests. This matters when you’re not just predicting house prices but explaining to stakeholders why square footage matters more than the number of bathrooms.

Start with the simplest case: one predictor variable. Here’s a complete example using car data to predict fuel efficiency: Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See Module Reference for commands and arguments. \(Y = X\beta + \epsilon\), where \(\epsilon\sim N\left(0,\Sigma\right).\)

Depending on the properties of \(\Sigma\), we have currently four classes available: GLS : generalized least squares for arbitrary covariance \(\Sigma\) We will be using the statsmodels package in Python, so we will need to import this along with the other Python packages we have been using. As usual, run the code cell below to import the relevant Python libraries If that threw an error, you may need to install statsmodels before you can import it. … and then rerun the importing block above

The python code, once you get the hang of it, is pretty straightforward. The output that Python gives is a whole table of statistics, some of which are important to us in this class, and some will be important later, and some we won’t need in the... In this article, we will discuss how to use statsmodels using Linear Regression in Python. Linear regression analysis is a statistical technique for predicting the value of one variable(dependent variable) based on the value of another(independent variable). The dependent variable is the variable that we want to predict or forecast. In simple linear regression, there's one independent variable used to predict a single dependent variable.

In the case of multilinear regression, there's more than one independent variable. The independent variable is the one you're using to forecast the value of the other variable. The statsmodels.regression.linear_model.OLS method is used to perform linear regression. Linear equations are of the form: Syntax: statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) Return: Ordinary least squares are returned.

Importing the required packages is the first step of modeling. The pandas, NumPy, and stats model packages are imported. Textbook for Data 88E: Economic Models at UC Berkeley Content is stored in the content folder. Order of textbook can be changed from _toc.yml file. This will output HTML to _build/html, which can be copied to docs in order to be served on GitHub Pages.

The figures generated in this notebook are Plotly-based javascript-enbedded html files. The script portion of each figX.html file messes with the Jupyter Books ability to render the Latex symbols. You can see I commented out the creation of the html files(e.g fig1.html, etc). Then I went into each html file and commented out the script section. Then went back to the notebook and ran the cell to display the figure. There has to be an easier way!

Simple linear regression is a basic statistical method to understand the relationship between two variables. One variable is dependent, and the other is independent. Python’s statsmodels library makes linear regression easy to apply and understand. This article will show you how to perform simple linear regression using statsmodels. Simple Linear Regression is a statistical method that models the relationship between two variables. The general equation for a simple linear regression is:

This equation represents a straight-line relationship. Changes in X lead to proportional changes in Y. Simple linear regression helps to understand and measure this relationship. It is a fundamental technique in statistical modeling and machine learning. First, install statsmodels if you haven’t already: We will use a simple dataset where we analyze the relationship between advertising spending (X) and sales revenue (Y).

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Data 88E: Economic Models Is A Course Offered At UC

Data 88E: Economic Models is a course offered at UC Berkeley as part of the Data Science Connector Course series. It’s designed to bridge the gap between data science and economics by using real-world datasets and Python programming to illustrate key economic concepts. The course is intended for students from both data science and economics backgrounds, giving them a chance to apply computational ...

This Package Is Known As Datascience Tables. Furthermore The Notebooks

This package is known as datascience tables. Furthermore the notebooks also make extensive use of a Python package for automatic grading called otter-grader. Both packages are available from pypi using pip install XXX . I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understandi...

Let’s Work Through Linear Regression In Python Using Statsmodels, From

Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter. Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS). Think of it as the statistical counterpart to scikit-learn. Where scikit-lear...

Start With The Simplest Case: One Predictor Variable. Here’s A

Start with the simplest case: one predictor variable. Here’s a complete example using car data to predict fuel efficiency: Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalize...

Depending On The Properties Of \(\Sigma\), We Have Currently Four

Depending on the properties of \(\Sigma\), we have currently four classes available: GLS : generalized least squares for arbitrary covariance \(\Sigma\) We will be using the statsmodels package in Python, so we will need to import this along with the other Python packages we have been using. As usual, run the code cell below to import the relevant Python libraries If that threw an error, you may n...