stdout

'mass' column not found, unable to perform regression.


                            OLS Regression Results                            
==============================================================================
Dep. Variable:        rotation_period   R-squared:                       0.044
Model:                            OLS   Adj. R-squared:                 -0.015
Method:                 Least Squares   F-statistic:                    0.7375
Date:                Sat, 04 May 2024   Prob (F-statistic):              0.598
Time:                        16:59:33   Log-Likelihood:                -331.24
No. Observations:                  87   AIC:                             674.5
Df Residuals:                      81   BIC:                             689.3
Df Model:                           5                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             12.4764      6.495      1.921      0.058      -0.446      25.399
height             0.0493      0.037      1.321      0.190      -0.025       0.124
orbital_period     0.0002      0.006      0.038      0.970      -0.011       0.012
diameter           0.0001      0.000      0.431      0.668      -0.000       0.001
surface_water      0.0094      0.050      0.190      0.850      -0.089       0.108
population     -1.513e-12   6.49e-12     -0.233      0.816   -1.44e-11    1.14e-11
==============================================================================
Omnibus:                       57.441   Durbin-Watson:                   1.530
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              652.260
Skew:                           1.637   Prob(JB):                    2.31e-142
Kurtosis:                      16.008   Cond. No.                     1.05e+12
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.05e+12. This might indicate that there are
strong multicollinearity or other numerical problems.


<table class="simpletable">
<caption>OLS Regression Results</caption>
<tr>
  <th>Dep. Variable:</th>     <td>rotation_period</td> <th>  R-squared:         </th> <td>   0.044</td>
</tr>
<tr>
  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.015</td>
</tr>
<tr>
  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.7375</td>
</tr>
<tr>
  <th>Date:</th>             <td>Sat, 04 May 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.598</td> 
</tr>
<tr>
  <th>Time:</th>                 <td>16:59:33</td>     <th>  Log-Likelihood:    </th> <td> -331.24</td>
</tr>
<tr>
  <th>No. Observations:</th>      <td>    87</td>      <th>  AIC:               </th> <td>   674.5</td>
</tr>
<tr>
  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   689.3</td>
</tr>
<tr>
  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   
</tr>
<tr>
  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   
</tr>
</table>
<table class="simpletable">
<tr>
         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  
</tr>
<tr>
  <th>const</th>          <td>   12.4764</td> <td>    6.495</td> <td>    1.921</td> <td> 0.058</td> <td>   -0.446</td> <td>   25.399</td>
</tr>
<tr>
  <th>height</th>         <td>    0.0493</td> <td>    0.037</td> <td>    1.321</td> <td> 0.190</td> <td>   -0.025</td> <td>    0.124</td>
</tr>
<tr>
  <th>orbital_period</th> <td>    0.0002</td> <td>    0.006</td> <td>    0.038</td> <td> 0.970</td> <td>   -0.011</td> <td>    0.012</td>
</tr>
<tr>
  <th>diameter</th>       <td>    0.0001</td> <td>    0.000</td> <td>    0.431</td> <td> 0.668</td> <td>   -0.000</td> <td>    0.001</td>
</tr>
<tr>
  <th>surface_water</th>  <td>    0.0094</td> <td>    0.050</td> <td>    0.190</td> <td> 0.850</td> <td>   -0.089</td> <td>    0.108</td>
</tr>
<tr>
  <th>population</th>     <td>-1.513e-12</td> <td> 6.49e-12</td> <td>   -0.233</td> <td> 0.816</td> <td>-1.44e-11</td> <td> 1.14e-11</td>
</tr>
</table>
<table class="simpletable">
<tr>
  <th>Omnibus:</th>       <td>57.441</td> <th>  Durbin-Watson:     </th> <td>   1.530</td> 
</tr>
<tr>
  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 652.260</td> 
</tr>
<tr>
  <th>Skew:</th>          <td> 1.637</td> <th>  Prob(JB):          </th> <td>2.31e-142</td>
</tr>
<tr>
  <th>Kurtosis:</th>      <td>16.008</td> <th>  Cond. No.          </th> <td>1.05e+12</td> 
</tr>
</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.05e+12. This might indicate that there are<br/>strong multicollinearity or other numerical problems.

\begin{center}
\begin{tabular}{lclc}
\toprule
\textbf{Dep. Variable:}    & rotation\_period & \textbf{  R-squared:         } &     0.044   \\
\textbf{Model:}            &       OLS        & \textbf{  Adj. R-squared:    } &    -0.015   \\
\textbf{Method:}           &  Least Squares   & \textbf{  F-statistic:       } &    0.7375   \\
\textbf{Date:}             & Sat, 04 May 2024 & \textbf{  Prob (F-statistic):} &    0.598    \\
\textbf{Time:}             &     16:59:33     & \textbf{  Log-Likelihood:    } &   -331.24   \\
\textbf{No. Observations:} &          87      & \textbf{  AIC:               } &     674.5   \\
\textbf{Df Residuals:}     &          81      & \textbf{  BIC:               } &     689.3   \\
\textbf{Df Model:}         &           5      & \textbf{                     } &             \\
\textbf{Covariance Type:}  &    nonrobust     & \textbf{                     } &             \\
\bottomrule
\end{tabular}
\begin{tabular}{lcccccc}
                         & \textbf{coef} & \textbf{std err} & \textbf{t} & \textbf{P$> |$t$|$} & \textbf{[0.025} & \textbf{0.975]}  \\
\midrule
\textbf{const}           &      12.4764  &        6.495     &     1.921  &         0.058        &       -0.446    &       25.399     \\
\textbf{height}          &       0.0493  &        0.037     &     1.321  &         0.190        &       -0.025    &        0.124     \\
\textbf{orbital\_period} &       0.0002  &        0.006     &     0.038  &         0.970        &       -0.011    &        0.012     \\
\textbf{diameter}        &       0.0001  &        0.000     &     0.431  &         0.668        &       -0.000    &        0.001     \\
\textbf{surface\_water}  &       0.0094  &        0.050     &     0.190  &         0.850        &       -0.089    &        0.108     \\
\textbf{population}      &   -1.513e-12  &     6.49e-12     &    -0.233  &         0.816        &    -1.44e-11    &     1.14e-11     \\
\bottomrule
\end{tabular}
\begin{tabular}{lclc}
\textbf{Omnibus:}       & 57.441 & \textbf{  Durbin-Watson:     } &     1.530  \\
\textbf{Prob(Omnibus):} &  0.000 & \textbf{  Jarque-Bera (JB):  } &   652.260  \\
\textbf{Skew:}          &  1.637 & \textbf{  Prob(JB):          } & 2.31e-142  \\
\textbf{Kurtosis:}      & 16.008 & \textbf{  Cond. No.          } &  1.05e+12  \\
\bottomrule
\end{tabular}
%\caption{OLS Regression Results}
\end{center}

Notes: \newline
 [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \newline
 [2] The condition number is large, 1.05e+12. This might indicate that there are \newline
 strong multicollinearity or other numerical problems.

<class 'statsmodels.iolib.summary.Summary'>
"""
                            OLS Regression Results                            
==============================================================================
Dep. Variable:        rotation_period   R-squared:                       0.044
Model:                            OLS   Adj. R-squared:                 -0.015
Method:                 Least Squares   F-statistic:                    0.7375
Date:                Sat, 04 May 2024   Prob (F-statistic):              0.598
Time:                        16:59:33   Log-Likelihood:                -331.24
No. Observations:                  87   AIC:                             674.5
Df Residuals:                      81   BIC:                             689.3
Df Model:                           5                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             12.4764      6.495      1.921      0.058      -0.446      25.399
height             0.0493      0.037      1.321      0.190      -0.025       0.124
orbital_period     0.0002      0.006      0.038      0.970      -0.011       0.012
diameter           0.0001      0.000      0.431      0.668      -0.000       0.001
surface_water      0.0094      0.050      0.190      0.850      -0.089       0.108
population     -1.513e-12   6.49e-12     -0.233      0.816   -1.44e-11    1.14e-11
==============================================================================
Omnibus:                       57.441   Durbin-Watson:                   1.530
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              652.260
Skew:                           1.637   Prob(JB):                    2.31e-142
Kurtosis:                      16.008   Cond. No.                     1.05e+12
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.05e+12. This might indicate that there are
strong multicollinearity or other numerical problems.
"""

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>5</td>
      <td>9</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>6</td>
      <td>10</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>7</td>
      <td>11</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>8</td>
      <td>12</td>
    </tr>
  </tbody>
</table>
</div>

   A  B   C
0  1  5   9
1  2  6  10
2  3  7  11
3  4  8  12

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