Summary Col Fails With Ols Regression After Calling Remove Data

Leo Migdal

-Dec 4, 2025, 9:48 AM

summary col fails with ols regression after calling remove data

There was an error while loading. Please reload this page. After fitting an OLS regression model and using remove_data() to reduce the model size, when I try to summarize the results using statsmodels.iolib.summary2.summary_col() I get the following error: Note: As you can see, there are many issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates. Note: Please be sure you are using the latest released version of statsmodels, or a recent build of master.

If your problem has been fixed in an unreleased version, you might be able to use master until a new release occurs. Note: If you are using a released version, have you verified that the bug exists in the master branch of this repository? It helps the limited resources if we know problems exist in the current master so that they do not need to check whether the code sample produces a bug in the next release. Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal.

Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. Linear regression is a popular method for understanding how different factors (independent variables) affect an outcome (dependent variable.

The Ordinary Least Squares (OLS) method helps us find the best-fitting line that predicts the outcome based on the data we have. In this article we will break down the key parts of the OLS summary and how to interpret them in a way that's easy to understand. Many statistical software options, like MATLAB, Minitab, SPSS, and R, are available for regression analysis, this article focuses on using Python. The OLS summary report is a detailed output that provides various metrics and statistics to help evaluate the model's performance and interpret its results. Understanding each one can reveal valuable insights into your model's performance and accuracy. The summary table of the regression is given below for reference, providing detailed information on the model's performance, the significance of each variable, and other key statistics that help in interpreting the results.

Here are the key components of the OLS summary: Where, N = sample size(no. of observations) and K = number of variables + 1 (including the intercept). \text{Standard Error} = \sqrt{\frac{N - K}{\text{Residual Sum of Squares}}} \cdot \sqrt{\frac{1}{\sum{(X_i - \bar{X})^2}}} This formula provides a measure of how much the coefficient estimates vary from sample to sample. The summary_col function in statsmodels makes nice regression tables easy to create.

When you add a categorical variable to your model, it automatically adds a variable for each level. Sometimes, these coefficients have meaning and are of interest. However, this isn’t always true. For example, in an earlier page noted that you can modify a model from \(profits=a+b*investment+c*X+u\), where the focus is on understanding how investments translate to profits, to \(profits=a+b*investment+c*X+d*C(gsector)+e*C(year)+u\). The latter model is better, but the coefficients on gsector and year are not the focus (and are difficult to interpret). Aside: When a categorical variable has many levels, it is often called a “fixed effect”.

So the latter model, which adds industry and year to a regression as a categorical variable, is said to include “industry fixed effects” and “year fixed effect”. The point of industry fixed effects is usually not to understand the coefficients on the industry dummy variables. It is to “control for industry”, and it changes the interpretation of \(b\): It is the relationship between investment and profits, holding fixed the industry. The same goes for the year fixed effects. Thus, in the improved model, \(b\) shows the relationship for two firms in the same industry in the same year. When a categorical variable has a lot of levels, and seeing those values is not important, the output tables are easier to read if you drop those coefficients.

There was an error while loading. Please reload this page. It would be great if a model saved with the data removed would still be able to produce the summary statistics. Potentially it's worthwhile keeping the minimum amount of data in order to reproduce these stats? Especially when using the formula API in which the original data is still saved, see here and here. Summarize multiple results instances side-by-side (coefs and SEs)

results : statsmodels results instance or list of result instances float format for coefficients and standard errors Default : ‘%.4f’ model_names : list of strings of length len(results) if the names are not unique, a roman number will be appended to all model names There was an error while loading. Please reload this page.

summary_col always appends two extra rows to the table, with the adjusted R-squared (incorrectly labelled R-squared) and the regular R-squared (unlabeled) whether you want them or not. In earlier versions (0.10 and below) this didn't happen. Installed: 0.11.1 (C:\Users\Axel\Anaconda3\lib\site-packages\statsmodels) cython: 0.29.15 (C:\Users\Axel\Anaconda3\lib\site-packages\Cython) numpy: 1.18.1 (C:\Users\Axel\Anaconda3\lib\site-packages\numpy) scipy: 1.2.1 (C:\Users\Axel\Anaconda3\lib\site-packages\scipy) pandas: 1.0.0 (C:\Users\Axel\Anaconda3\lib\site-packages\pandas) dateutil: 2.8.1 (C:\Users\Axel\Anaconda3\lib\site-packages\dateutil) patsy: 0.5.1 (C:\Users\Axel\Anaconda3\lib\site-packages\patsy) matplotlib: 3.0.3 (C:\Users\Axel\Anaconda3\lib\site-packages\matplotlib) backend: module://ipykernel.pylab.backend_inline cvxopt: Not installed joblib: 0.14.1 (C:\Users\Axel\Anaconda3\lib\site-packages\joblib) Ordinary Least Squares (OLS) is a widely used statistical method for estimating the parameters of a linear regression model.

It minimizes the sum of squared residuals between observed and predicted values. In this article we will learn how to implement Ordinary Least Squares (OLS) regression using Python's statsmodels module. A linear regression model establishes the relationship between a dependent variable (y) and one or more independent variables (x): The OLS method minimizes the total sum of squares of residuals (S) defined as: S = \sum_{i=1}^{n} \epsilon_i^2 = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 To find the optimal values of b0 and b1 partial derivatives of S with respect to each coefficient are taken and set to zero.

Summary Col Fails With Ols Regression After Calling Remove Data

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The Ordinary Least Squares (OLS) Method Helps Us Find The

Here Are The Key Components Of The OLS Summary: Where,