title: Different results from lm in R vs. statsmodel OLS in Python
body: <p>I'm new to Python and have been an R User. I am getting <strong>VERY</strong> different results from a simple regression model when I build it in R vs. when I execute the same thing in iPython. </p>
  
  <p>The R-Squared, The P Value , The significance of the co-efficients - nothing matches . Am I reading the output wrong or making some other fundamental error? </p>
  
  <p>Below are my codes for both and results: </p>
  
  <p><strong>R Code</strong></p>
  
  <pre><code>str(df_nv)
  Classes 'tbl_df', 'tbl' and 'data.frame':   81 obs. of  2 variables:
   $ Dependent Variabls       : num  733 627 405 353 434 556 381 558 612 901 ...
   $ Independent Variable: num  0.193 0.167 0.169 0.14 0.145 ...
  
  
  summary(lm(`Dependent Variable` ~ `Independent Variable`, data = df_nv))
  
  Call:
      lm(formula = `Dependent Variable` ~ `Independent Variable`, data = df_nv)
  
  
  Residuals:
      Min      1Q  Median      3Q     Max 
  -501.18 -139.20  -82.61  -15.82 2136.74 
  
  Coefficients:
                         Estimate Std. Error t value Pr(&gt;|t|)   
  (Intercept)               478.2      148.2   3.226  0.00183 **
  `Independent Variable`   -196.1     1076.9  -0.182  0.85601   
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  
  Residual standard error: 381.5 on 79 degrees of freedom
  Multiple R-squared:  0.0004194, Adjusted R-squared:  -0.01223 
  F-statistic: 0.03314 on 1 and 79 DF,  p-value: 0.856
  </code></pre>
  
  <p><strong>iPython Notebook Code</strong> </p>
  
  <pre><code>df_nv.dtypes
  
  Dependent Variable           float64
  Independent Variable         float64
  dtype: object
  
  model = sm.OLS(df_nv['Dependent Variable'], df_nv['Independent Variable'])
  
  results = model.fit()
  results.summary()
  
  OLS Regression Results
  Dep. Variable:  Dependent Variable  R-squared:  0.537
  Model:  OLS Adj. R-squared: 0.531
  Method: Least Squares   F-statistic:    92.63
  Date:   Fri, 20 Jan 2017    Prob (F-statistic): 5.23e-15
  Time:   09:08:54    Log-Likelihood: -600.40
  No. Observations:   81  AIC:    1203.
  Df Residuals:   80  BIC:    1205.
  Df Model:   1       
  Covariance Type:    nonrobust       
  coef    std err t   P&gt;|t|   [95.0% Conf. Int.]
  Independent Variable    3133.1825   325.537 9.625   0.000   2485.342 3781.023
  Omnibus:    89.595  Durbin-Watson:  1.940
  Prob(Omnibus):  0.000   Jarque-Bera (JB):   980.289
  Skew:   3.489   Prob(JB):   1.36e-213
  Kurtosis:   18.549  Cond. No.   1.00
  </code></pre>
  
  <p>For reference, head of dataframe in both R and Python : </p>
  
  <p>R: </p>
  
  <pre><code>head(df_nv)
    Dependent Variable Independent Variable
            &lt;dbl&gt;                &lt;dbl&gt;
  1           733            0.1932367
  2           627            0.1666667
  3           405            0.1686183
  4           353            0.1398601
  5           434            0.1449275
  6           556            0.1475410
  </code></pre>
  
  <p>Python: </p>
  
  <pre><code>df_nv.head()
  
      Dependent Variable  Independent Variable
  5292    733.0   0.193237
  5320    627.0   0.166667
  5348    405.0   0.168618
  5404    353.0   0.139860
  5460    434.0   0.144928
  </code></pre>