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 ===============================================================================

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 1.5.0rc0 (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, 24 Aug 2022 Prob 11:20:55 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Wed, 29 Jan 2020 Prob analysis toolkit, Release 1.0.0 (continued from previous page) 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

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 ===============================================================================

started pandas: powerful Python data analysis toolkit, Release 1.0.5 (continued from previous page) Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Wed, 17 Jun 2020 Prob 17:43:40 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

started pandas: powerful Python data analysis toolkit, Release 1.0.4 (continued from previous page) Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Thu, 28 May 2020 Prob 20:44:27 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

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: Thu, 20 Aug 2020 Prob 19:37:10 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 R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Tue, 28 Jul 2020 Prob 14:50:11 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust ===============================================================================

started pandas: powerful Python data analysis toolkit, Release 1.0.3 (continued from previous page) Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Wed, 18 Mar 2020 Prob 15:38:44 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

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: Tue, 02 Mar 2021 Prob pandas: powerful Python data analysis toolkit, Release 1.2.3 (continued from previous page) Df Model: 4 Covariance Type: nonrobust ===============================================================================