# Statsmodels linear regression diagnostics

The 4 peace-keepers. There are four principal assumptions which support using a **linear regression** model for the purpose of inference or prediction: Linearity: Sounds obvious! We must have a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased.

Jan 14, 2020 · Lagrange Multiplier test for Null hypothesis that **linear** specification is correct. This tests against specific functional alternatives. :py:func:`spec_white <**statsmodels**.stats.**diagnostic**.spec_white>`. White's two-moment specification test with null hypothesis of homoscedastic and correctly specified.. Builiding the Logistic **Regression** model : **Statsmodels** is a Python module that provides various functions for estimating different statistical models and performing statistical tests. First, we define the set of dependent ( y) and independent ( X) variables. If the dependent variable is in non-numeric form, it is first converted to numeric using. Logistic **Regression** with **statsmodels**. Before starting, it's worth mentioning there are two ways to do Logistic **Regression** in **statsmodels**: **statsmodels**.api: The Standard API. Data gets separated into explanatory variables ( exog) and a response variable ( endog ). Specifying a model is done through classes. **statsmodels**.formula.api: The Formula API.

Mar 02, 2021 · **Linear** **regression** models play a huge role in the analytics and decision-making process of many companies, owing in part to their ease of use and interpretability. There are instances, however, that the presence of certain data points affects the predictive power of such models. These data points are known as influential points.. Jan 14, 2020 · Lagrange Multiplier test for Null hypothesis that **linear** specification is correct. This tests against specific functional alternatives. :py:func:`spec_white <**statsmodels**.stats.**diagnostic**.spec_white>`. White's two-moment specification test with null hypothesis of homoscedastic and correctly specified.. # Autogenerated from the notebook **regression**_**diagnostics**.ipynb. # Edit the notebook and then sync the output with this file. # # flake8: noqa # DO NOT EDIT # # **Regression** **diagnostics** # This example file shows how to use a few of the ``**statsmodels**`` # **regression** **diagnostic** tests in a real-life context. You can learn about.

Course Description. **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 you need to fit simple **linear** and logistic **regressions**. Through hands-on exercises, you'll explore the relationships. Option 2: Omni test. If you are looking for a variety of (scaled) residuals such as externally/internally studentized residuals, PRESS residuals and others, take a look at the OLSInfluence class within **statsmodels**. Using the results (a RegressionResults object) from your fit, you instantiate an OLSInfluence object that will have all of these.

Rolling **Regression**. Rolling OLS applies OLS across a fixed windows of observations and then rolls (moves or slides) the window across the data set. They key parameter is window which determines the number of observations used in each OLS **regression**. By default, RollingOLS drops missing values in the window and so will estimate the model using.

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These 4 plots examine a few different assumptions about the model and the data: 1) The data can be fit by a line (this includes any transformations made to the predictors, e.g., x2 x 2 or √x x) 2) Errors are normally distributed with mean zero. 3) Errors have constant variance, i.e., homoscedasticity. 4) There are no high leverage points. We have completed our multiple **linear** **regression** model. If we want more of detail, we can perform multiple **linear** **regression** analysis using **statsmodels**. **Statsmodels** is a Python module that provides classes and functions for the estimation of different statistical models, as well as different statistical tests.

3.11.8. **statsmodels.stat**s.diagnostic. 3.11.8.1. Functions. Breush Godfrey Lagrange Multiplier tests for residual autocorrelation. test for model stability, breaks in parameters for ols, Hansen 1992. het_arch (resid [, maxlag, autolag, store, ...]) Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH) Breush-Pagan Lagrange.

2022. 5. 31. ·

Regressiondiagnostics. This example file shows how to use a few of thestatsmodelsregressiondiagnostictests in a real-life context. You can learn about more tests and find out more information abou the tests here on theRegressionDiagnosticspage.. Note that most of the tests described here only return a tuple of numbers, without any annotation.

**statsmodels**.stats.**diagnostic**.**linear**_harvey_collier (res) [source] Harvey Collier test for linearity. The Null hypothesis is that the **regression** is correctly modeled as **linear**. Parameters: res ( Result instance) –. Returns: tvalue ( float) – test statistic, based on ttest_1sample. pvalue ( float) – pvalue of the test.

Nov 21, 2020 · 3. Create **linear** **regression** model. We will use the **Statsmodels** library for **linear** **regression**. (Scikit-learn can also be used as an alternative but here I preferred **statsmodels** to reach a more detailed analysis of the **regression** model). Having said that, we will still be using Scikit-learn for train-test split..

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**Linear** **regression** **diagnostics** In real-life, relation between response and target variables are seldom **linear**. Here, we make use of outputs of **statsmodels** to visualise and identify potential problems that can occur from fitting **linear** **regression** model to non-**linear** relation.

**statsmodels**.stats.**diagnostic**.linear_lm(resid, exog, func=None)[source] Lagrange multiplier test for linearity against functional alternative # TODO: Remove the restriction limitations: Assumes currently that the first column is integer. Currently it does not check whether the transformed variables contain NaNs, for example log of negative number. This post will walk you through building **linear** **regression** models to predict housing prices resulting from economic activity. Future posts will cover related topics such as exploratory analysis, **regression** **diagnostics**, and advanced **regression** modeling, but I wanted to jump right in so readers could get their hands dirty with data. About** statsmodels** Developer Page Release Notes** Regression diagnostics** Estimate a** regression model** Normality of the residuals Influence tests Multicollinearity Heteroskedasticity tests** Linearity** Show Source** Regression diagnostics** This example file shows how to use a few of the** statsmodels regression diagnostic tests** in a real-life context..

**Linear** **regression** **Linear** **regression** **Regression** **diagnostics** Lasso, Splines & GAM Hitters data preparation Lasso **regression** ... % matplotlib inline import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import **statsmodels**.formula.api as smf from **statsmodels**.tools.eval_measures import mse, rmse sns. set_theme.

. # Autogenerated from the notebook **regression**_**diagnostics**.ipynb. # Edit the notebook and then sync the output with this file. # # flake8: noqa # DO NOT EDIT # # **Regression diagnostics** # This example file shows how to use a few of the ``**statsmodels**`` # **regression diagnostic** tests in a real-life context. You can learn about. Chapter Outline. 2.0 **Regression Diagnostics**. 2.1 Unusual and Influential data. 2.2 Checking Normality of Residuals. 2.3 Checking Homoscedasticity. 2.4 Checking for Multicollinearity. 2.5 Checking Linearity. 2.6 Model Specification. 2.7 Issues of Independence. the are called the errors. We have five main assumptions for **linear** **regression**. Linearity: there is a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased. No multicollinearity: our features are not correlated.

There are five different types of Assumptions in **linear** **regression**. **Linear** Relationship, No Autocorrelation, Multivariate Normality, Homoscedasticity ... above as it forms a straight line on which most of the points are lying so **linear** # Rainbow Test for Linearity import **statsmodels**.api as sm sm.stats.**diagnostic**.linear_rainbow(res=lin_reg) #2nd. However, the success of a **linear** **regression** model also depends on some fundamental assumptions about the nature of the underlying data that it tries to model. See this article for a simple and intuitive understanding of these assumptions, ... Other residuals **diagnostics**. **Statsmodels** have a wide variety of other **diagnostics** tests for checking. Rolling **Regression**. Rolling OLS applies OLS across a fixed windows of observations and then rolls (moves or slides) the window across the data set. They key parameter is window which determines the number of observations used in each OLS **regression**. By default, RollingOLS drops missing values in the window and so will estimate the model using. Dec 30, 2019 · Cooks ¶. is a commonly used estimation of the data point impact when performing the least squares **regression** analysis. [1] In practical ordinary least squares analysis, the Cook distance can be used in several ways: indicate areas of the design space in which it would be good to obtain more data points..

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As I mentioned in the comments, seaborn is a great choice for statistical data visualization. import seaborn as sns sns.regplot (x='motifScore', y='expression', data=motif) Alternatively, you can use **statsmodels**.**regression**.linear_model.OLS and manually plot a **regression** line. import **statsmodels**.api as sm # regress "expression" onto "motifScore. **Rolling Regression**. Rolling OLS applies OLS across a fixed windows of observations and then rolls (moves or slides) the window across the data set. They key parameter is window which determines the number of observations used in each OLS **regression**. By default, RollingOLS drops missing values in the window and so will estimate the model using. One of the most essential steps to take before applying **linear** **regression** and depending solely on accuracy scores is to check for these assumptions. ¶. Table of Content. 1. Linearity. 2. Mean of Residuals. 3. Check for Homoscedasticity. Ordinary least squares **Linear** **Regression**. **LinearRegression** fits a **linear** model with coefficients w = (w1, , wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the **linear** approximation. Whether to calculate the intercept for this model.

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Chapter Outline. 2.0 **Regression** **Diagnostics**. 2.1 Unusual and Influential data. 2.2 Checking Normality of Residuals. 2.3 Checking Homoscedasticity. 2.4 Checking for Multicollinearity. 2.5 Checking Linearity. 2.6 Model Specification. 2.7 Issues of Independence. The 4 peace-keepers. There are four principal assumptions which support using a **linear regression** model for the purpose of inference or prediction: Linearity: Sounds obvious! We must have a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased. 2 days ago · Multivariate **Linear** **Regression** Using Scikit Learn Introduction neural_network), but this one takes us deeper in the world of neural networks and the algorithms involved in them If you are looking for how to run code jump to the next section or if you would like some theory/refresher then start with this section Python users are incredibly lucky to have so many. 2020.

This involves two aspects, as we are dealing with the two sides of our logistic **regression** equation. First, consider the link function of the outcome variable on the left hand side of the equation. We assume that the logit function (in logistic **regression**) is the correct function to use. Secondly, on the right hand side of the equation, we .... . Last updated on Nov 14, 2021 18 min read Python, **Regression**. **Linear regression diagnostics** in Python. When we fit a **linear regression** model to a particular data set, many problems may occur. Most common among these are the following (James et al., 2021): High-leverage points. Non-linearity of the response-predictor relationship.

**Regression diagnostics**¶. This example file shows how to use a few of the **statsmodels regression diagnostic** tests in a real-life context. You can learn about more tests and find out more information about the tests here on the **Regression Diagnostics** page.. Note that most of the tests described here only return a tuple of numbers, without any annotation.

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About** statsmodels** Developer Page Release Notes** Regression diagnostics** Estimate a** regression model** Normality of the residuals Influence tests Multicollinearity Heteroskedasticity tests** Linearity** Show Source** Regression diagnostics** This example file shows how to use a few of the** statsmodels regression diagnostic tests** in a real-life context.. I learnt this abbreviation of **linear regression** assumptions when I was taking a course on correlation and **regression** taught by Walter Vispoel at UIowa. Really helped me to remember these four little things! In fact, **statsmodels** itself contains useful modules for **regression diagnostics**. In addition to those, I want to go with somewhat manual yet. 3.11.8. **statsmodels.stat**s.diagnostic. 3.11.8.1. Functions. Breush Godfrey Lagrange Multiplier tests for residual autocorrelation. test for model stability, breaks in parameters for ols, Hansen 1992. het_arch (resid [, maxlag, autolag, store, ...]) Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH) Breush-Pagan Lagrange.

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Jan 14, 2020 · Lagrange Multiplier test for Null hypothesis that **linear** specification is correct. This tests against specific functional alternatives. :py:func:`spec_white <**statsmodels**.stats.**diagnostic**.spec_white>`. White's two-moment specification test with null hypothesis of homoscedastic and correctly specified.. **Regression diagnostics**¶. This example file shows how to use a few of the **statsmodels regression diagnostic** tests in a real-life context. You can learn about more tests and find out more information about the tests here on the **Regression Diagnostics** page.. Note that most of the tests described here only return a tuple of numbers, without any annotation.

acorr_linear_lm. Lagrange Multiplier test for Null hypothesis that **linear** specification is correct. This tests against specific functional alternatives. Tests for Structural Change, Parameter Stability. Test whether all or some **regression** coefficient are constant over the entire data sample. Known Change Point OneWayLS :.

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Today, in multiple **linear regression** in **statsmodels**, we expand this concept by fitting our (p) predictors to a (p)-dimensional hyperplane. Multiple **Linear Regression** Equation: Let’s understand the equation: y – dependent variable. b 0 – refers to the point on the Y-axis where the Simple **Linear Regression** Line crosses it. 9. · **Linear** **regression** **Diagnostics** **Regression** **diagnostics** Lasso, Splines & GAM Hitters data preparation Lasso **regression** **Regression** splines Generalized Additive Models (GAM) Decision Trees Gradient Boosting Feature Selection Feature selection Ensemble Ensemble meta-estimator Case Duke Case study Data Create data prep file. 2021. This example file shows how to use a few of the **statsmodels** **regression** **diagnostic** tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the **Regression** **Diagnostics** page. Note that most of the tests described here only return a tuple of numbers, without any annotation.

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The core of **statsmodels** is "production ready": **linear** models, robust **linear** models, generalised **linear** models and discrete models have been around for several years and are verified against Stata and R. **statsmodels** also has a time series analysis part covering AR, ARMA and VAR (vector autoregressive) **regression**, which are not available in any.

About** statsmodels** Developer Page Release Notes** Regression diagnostics** Estimate a** regression model** Normality of the residuals Influence tests Multicollinearity Heteroskedasticity tests** Linearity** Show Source** Regression diagnostics** This example file shows how to use a few of the** statsmodels regression diagnostic tests** in a real-life context.. We’re all set, so onto the assumption testing! Assumptions Permalink. I) Linearity Permalink. This assumes that there is a **linear** relationship between the predictors (e.g. independent variables or features) and the response variable (e.g. dependent variable or label). This also assumes that the predictors are additive. Logistic **Regression** with **statsmodels**. Before starting, it's worth mentioning there are two ways to do Logistic **Regression** in **statsmodels**: **statsmodels**.api: The Standard API. Data gets separated into explanatory variables ( exog) and a response variable ( endog ). Specifying a model is done through classes. **statsmodels**.formula.api: The Formula API.

Search: **Statsmodels** Prediction Interval. Covers nonparametric statistical hypothesis testing methods for use when data does not meet the expectations of parametric tests The estimation of parameters is done using the 'leastq' method from scipy Confidence interval (limits) calculator, formulas & workout with steps to measure or estimate confidence limits for the mean or.

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Builiding the Logistic **Regression** model : **Statsmodels** is a Python module that provides various functions for estimating different statistical models and performing statistical tests. First, we define the set of dependent ( y) and independent ( X) variables. If the dependent variable is in non-numeric form, it is first converted to numeric using.

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Dec 07, 2017 · **Poisson Regression** in **statsmodels** and R. With R, the **poisson** glm and **diagnostics** plot can be achieved as such: > col=2 > row=50 > range=0:100 > df <- data.frame (replicate (col,sample (range,row,rep=TRUE))) > model <- glm (X2 ~ X1, data = df, family = **poisson**) > glm.diag.plots (model) In Python, this would give me the line predictor vs residual .... This involves two aspects, as we are dealing with the two sides of our logistic **regression** equation. First, consider the link function of the outcome variable on the left hand side of the equation. We assume that the logit function (in logistic **regression**) is the correct function to use. Secondly, on the right hand side of the equation, we ....

**Regression** **diagnostics** When we fit a **linear** **regression** model to a particular data set, many problems may occur. Most common among these are the following [ James et al., 2021]: Outliers and high-leverage points Non-linearity of the response-predictor relationship Non-constant variance of error terms (heteroscedasticity). Sandbox: **statsmodels** contains a sandbox folder with code in various stages of development and testing which is not considered "production ready". This covers among others. Generalized method of moments (GMM) estimators. Kernel **regression**. Various extensions to scipy.stats.distributions. This example file shows how to use a few of the **statsmodels** **regression** **diagnostic** tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the **Regression Diagnostics** page. Note that most of the tests described here only return a tuple of numbers, without any annotation..

**Linear** **regression** is simple, with **statsmodels**. We are able to use R style **regression** formula. > import **statsmodels**.formula.api as smf > reg = smf.ols('adjdep ~ adjfatal + adjsimp', data=df).fit() > reg.summary() **Regression** assumptions Now let's try to validate the four assumptions one by one Linearity & Equal variance. **statsmodels** is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. ... **Linear** **regression** models: Ordinary least squares; Generalized least squares; ... **diagnostics** and specification tests; goodness-of-fit and normality tests;. for the logistic **regression** model is DEV = −2 Xn i=1 [Y i log(ˆπ i)+(1−Y i)log(1−πˆ i)], where πˆ i is the ﬁtted values for the ith observation. The smaller the deviance, the closer the ﬁtted value is to the saturated model. The larger the deviance, the poorer the ﬁt. BIOST 515, Lecture 14 2. Dec 31, 2016 · 2.0 **Regression** **Diagnostics**. When run **regression** models, you need to do **regression** disgnostics. Without verifying that your data have met the **regression** assumptions, your results may be misleading. This section will explore how to do **regression** **diagnostics**. Linearity - the relationships between the predictors and the outcome variable should be ....

acorr_**linear**_lm. Lagrange Multiplier test for Null hypothesis that **linear** specification is correct. This tests against specific functional alternatives. Tests for Structural Change, Parameter Stability. Test whether all or some **regression** coefficient are constant over the entire data sample. Known Change Point OneWayLS :. One of the most essential steps to take before applying **linear** **regression** and depending solely on accuracy scores is to check for these assumptions. ¶. Table of Content. 1. Linearity. 2. Mean of Residuals. 3. Check for Homoscedasticity.

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Jun 04, 2022 · 主要涉及到python的pandas、statsmodels、joblib等模块，通过对多个模型进行并行网格搜索寻找评价指标MAPE最小的模型参数，虽然供应链销量预测可供使用的模型非常多，但是作为计量经济学主要内容之一，时间序列因为其强大成熟完备的理论基础，应作为我们处理 .... # Autogenerated from the notebook **regression_diagnostics**.ipynb. Jan 06, 2016 · Again, the assumptions for **linear** **regression** are: Linearity: The relationship between X and the mean of Y is **linear**. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. Normality: For any fixed value of X, Y is normally distributed..

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Jul 05, 2020 · The 4 peace-keepers. There are four principal assumptions which support using a **linear** **regression** model for the purpose of inference or prediction: Linearity: Sounds obvious! We must have a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased..

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the are called the errors. We have five main assumptions for **linear** **regression**. Linearity: there is a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased. No multicollinearity: our features are not correlated.

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Last updated on Nov 14, 2021 18 min read Python, **Regression**. **Linear regression diagnostics** in Python. When we fit a **linear regression** model to a particular data set, many problems may occur. Most common among these are the following (James et al., 2021): High-leverage points. Non-linearity of the response-predictor relationship.

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This involves two aspects, as we are dealing with the two sides of our logistic **regression** equation. First, consider the link function of the outcome variable on the left hand side of the equation. We assume that the logit function (in logistic **regression**) is the correct function to use. Secondly, on the right hand side of the equation, we .... The lmfit algorithm is another wrapper around scipy.optimize.leastsq but allows for richer model specification and more **diagnostics**. In [99]: ... Given some data, one simple probability model is \(p(x) = \beta_0 + x\cdot\beta\) - i.e. **linear** **regression**. ... only using Python's **statsmodels** package. The GLM solver uses a special variant of.

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Feb 08, 2022 · **statsmodels** is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Documentation The documentation for the latest release is at. 3.11.8. **statsmodels.stat**s.diagnostic. 3.11.8.1. Functions. Breush Godfrey Lagrange Multiplier tests for residual autocorrelation. test for model stability, breaks in parameters for ols, Hansen 1992. het_arch (resid [, maxlag, autolag, store, ...]) Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH) Breush-Pagan Lagrange.

As I mentioned in the comments, seaborn is a great choice for statistical data visualization. import seaborn as sns sns.regplot (x='motifScore', y='expression', data=motif) Alternatively, you can use **statsmodels**.**regression**.**linear**_model.OLS and manually plot a **regression** line. import **statsmodels**.api as sm # regress "expression" onto "motifScore ....

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# Autogenerated from the notebook **regression**_**diagnostics**.ipynb. # Edit the notebook and then sync the output with this file. # # flake8: noqa # DO NOT EDIT # # **Regression diagnostics** # This example file shows how to use a few of the ``**statsmodels**`` # **regression diagnostic** tests in a real-life context. You can learn about. Jul 05, 2020 · The 4 peace-keepers. There are four principal assumptions which support using a **linear** **regression** model for the purpose of inference or prediction: Linearity: Sounds obvious! We must have a **linear** relationship between our features and responses. This is required for our estimator and predictions to be unbiased..

**statsmodels**.stats.**diagnostic**.**linear**_harvey_collier (res) [source] Harvey Collier test for linearity. The Null hypothesis is that the **regression** is correctly modeled as **linear**. Parameters: res ( Result instance) –. Returns: tvalue ( float) – test statistic, based on ttest_1sample. pvalue ( float) – pvalue of the test. Sep 29, 2018 · Yes, but you'll have to first generate the predictions with your model and then use the rmse method. from **statsmodels**.tools.eval_measures import rmse # fit your model which you have already done # now generate predictions ypred = model.predict (X) # calc rmse rmse = rmse (y, ypred) As for interpreting the results, HDD isn't the intercept..

Nov 21, 2020 · 3. Create **linear** **regression** model. We will use the **Statsmodels** library for **linear** **regression**. (Scikit-learn can also be used as an alternative but here I preferred **statsmodels** to reach a more detailed analysis of the **regression** model). Having said that, we will still be using Scikit-learn for train-test split..

3. Create **linear** **regression** model. We will use the **Statsmodels** library for **linear** **regression**. (Scikit-learn can also be used as an alternative but here I preferred **statsmodels** to reach a more detailed analysis of the **regression** model). Having said that, we will still be using Scikit-learn for train-test split. Nov 21, 2020 · 3. Create **linear** **regression** model. We will use the **Statsmodels** library for **linear** **regression**. (Scikit-learn can also be used as an alternative but here I preferred **statsmodels** to reach a more detailed analysis of the **regression** model). Having said that, we will still be using Scikit-learn for train-test split..

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Rolling **Regression**. Rolling OLS applies OLS across a fixed windows of observations and then rolls (moves or slides) the window across the data set. They key parameter is window which determines the number of observations used in each OLS **regression**. By default, RollingOLS drops missing values in the window and so will estimate the model using.