def regress(dependent_variable, explanatory_variables, alpha=True):
    "\n    Runs a linear regression to decompose the dependent variable into the explanatory variables\n    returns an object of type statsmodel's RegressionResults on which you can call\n       .summary() to print a full summary\n       .params for the coefficients\n       .tvalues and .pvalues for the significance levels\n       .rsquared_adj and .rsquared for quality of fit\n    "
    if alpha:
        explanatory_variables = explanatory_variables.copy()
        explanatory_variables['Alpha'] = 1
    lm = sm.OLS(dependent_variable, explanatory_variables).fit()
    return lm
Runs a linear regression to decompose the dependent variable into the explanatory variables
returns an object of type statsmodel's RegressionResults on which you can call
   .summary() to print a full summary
   .params for the coefficients
   .tvalues and .pvalues for the significance levels
   .rsquared_adj and .rsquared for quality of fit
def regress(dependent_variable, explanatory_variables, alpha=True):
    "\n    Runs a linear regression to decompose the dependent variable into the explanatory variables\n    returns an object of type statsmodel's RegressionResults on which you can call\n       .summary() to print a full summary\n       .params for the coefficients\n       .tvalues and .pvalues for the significance levels\n       .rsquared_adj and .rsquared for quality of fit\n    "
    if alpha:
        explanatory_variables = explanatory_variables.copy()
        explanatory_variables['Alpha'] = 1
    lm = sm.OLS(dependent_variable, explanatory_variables).fit()
    return lm