25 pandas: powerful Python data analysis toolkit, Release 0.7.3 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important DataFrame (panel model) • x: Series, DataFrame, dict of Series, dict of DataFrame or Panel Based on the types of y and x, the model will be inferred to either a panel model or a regular linear model. If the y y variable is a DataFrame, the result will be a panel model. In this case, the x variable must either be a Panel, or a dict of DataFrame (which will be coerced into a Panel). 8.4.1 Standard OLS regression

21 pandas: powerful Python data analysis toolkit, Release 0.7.1 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important DataFrame (panel model) • x: Series, DataFrame, dict of Series, dict of DataFrame or Panel Based on the types of y and x, the model will be inferred to either a panel model or a regular linear model. If the y y variable is a DataFrame, the result will be a panel model. In this case, the x variable must either be a Panel, or a dict of DataFrame (which will be coerced into a Panel). 8.4.1 Standard OLS regression

21 pandas: powerful Python data analysis toolkit, Release 0.7.2 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important DataFrame (panel model) • x: Series, DataFrame, dict of Series, dict of DataFrame or Panel Based on the types of y and x, the model will be inferred to either a panel model or a regular linear model. If the y y variable is a DataFrame, the result will be a panel model. In this case, the x variable must either be a Panel, or a dict of DataFrame (which will be coerced into a Panel). 8.4.1 Standard OLS regression

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sat, 02 Nov 2019 Prob 16:04:51 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:59:49 Log-Likelihood: 247 pandas: powerful Python data analysis toolkit, Release 0.17.0 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important Functionality pandas: powerful Python data analysis toolkit, Release 0.17.0 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:16:35 Log-Likelihood:

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which ============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sun, 15 Aug 2021 Prob pandas: powerful Python data analysis toolkit, Release 1.3.2 (continued from previous page) Df Model: 4 Covariance Type: nonrobust ===============================================================================

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which analysis toolkit, Release 1.3.3 (continued from previous page) Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sun, 12 Sep 2021 Prob 10:50:36 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which analysis toolkit, Release 1.3.4 (continued from previous page) Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sun, 17 Oct 2021 Prob 14:57:32 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which analysis toolkit, Release 1.4.2 (continued from previous page) Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sat, 02 Apr 2022 Prob 08:21:00 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which analysis toolkit, Release 1.4.4 (continued from previous page) Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Wed, 31 Aug 2022 Prob 09:57:41 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================