Is there a way to calculate an Covariate-Adjusted Pearon's Coefficient Correlation Value?

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Suppose I have data like this:



import pandas as pd
import statsmodels.formula.api as smf

sample_data = pd.read_csv('sample_data.csv',index_col = 0,header = 0)
sample_data


enter image description here



I want to explore the effects of Age on the Counts. If I want to remove the effect of sex from my data, I would perform a two step regression, using the residuals of my Counts vs. Sex and for a regression of residuals vs. Age like this:



sex_corrl = smf.ols('Counts ~ Sex',data = sample_data).fit()
sample_data['Residuals of Sex'] = list(sex_corrl.resid)
sex_adj_corrl = smf.ols('Q("Residuals of Sex") ~ Age',data = sample_data).fit()

sex_adj_corrl.summary()


enter image description here



My question is, if I take the square-root of the R2 value (the 0.013 value) at the end of the 2-step regression analysis, is it Pearson's Correlation Coefficient? If not, how would I calculate a sex-adjusted Pearson's Correlation Coefficient?










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    0















    Suppose I have data like this:



    import pandas as pd
    import statsmodels.formula.api as smf

    sample_data = pd.read_csv('sample_data.csv',index_col = 0,header = 0)
    sample_data


    enter image description here



    I want to explore the effects of Age on the Counts. If I want to remove the effect of sex from my data, I would perform a two step regression, using the residuals of my Counts vs. Sex and for a regression of residuals vs. Age like this:



    sex_corrl = smf.ols('Counts ~ Sex',data = sample_data).fit()
    sample_data['Residuals of Sex'] = list(sex_corrl.resid)
    sex_adj_corrl = smf.ols('Q("Residuals of Sex") ~ Age',data = sample_data).fit()

    sex_adj_corrl.summary()


    enter image description here



    My question is, if I take the square-root of the R2 value (the 0.013 value) at the end of the 2-step regression analysis, is it Pearson's Correlation Coefficient? If not, how would I calculate a sex-adjusted Pearson's Correlation Coefficient?










    share|improve this question

























      0












      0








      0








      Suppose I have data like this:



      import pandas as pd
      import statsmodels.formula.api as smf

      sample_data = pd.read_csv('sample_data.csv',index_col = 0,header = 0)
      sample_data


      enter image description here



      I want to explore the effects of Age on the Counts. If I want to remove the effect of sex from my data, I would perform a two step regression, using the residuals of my Counts vs. Sex and for a regression of residuals vs. Age like this:



      sex_corrl = smf.ols('Counts ~ Sex',data = sample_data).fit()
      sample_data['Residuals of Sex'] = list(sex_corrl.resid)
      sex_adj_corrl = smf.ols('Q("Residuals of Sex") ~ Age',data = sample_data).fit()

      sex_adj_corrl.summary()


      enter image description here



      My question is, if I take the square-root of the R2 value (the 0.013 value) at the end of the 2-step regression analysis, is it Pearson's Correlation Coefficient? If not, how would I calculate a sex-adjusted Pearson's Correlation Coefficient?










      share|improve this question














      Suppose I have data like this:



      import pandas as pd
      import statsmodels.formula.api as smf

      sample_data = pd.read_csv('sample_data.csv',index_col = 0,header = 0)
      sample_data


      enter image description here



      I want to explore the effects of Age on the Counts. If I want to remove the effect of sex from my data, I would perform a two step regression, using the residuals of my Counts vs. Sex and for a regression of residuals vs. Age like this:



      sex_corrl = smf.ols('Counts ~ Sex',data = sample_data).fit()
      sample_data['Residuals of Sex'] = list(sex_corrl.resid)
      sex_adj_corrl = smf.ols('Q("Residuals of Sex") ~ Age',data = sample_data).fit()

      sex_adj_corrl.summary()


      enter image description here



      My question is, if I take the square-root of the R2 value (the 0.013 value) at the end of the 2-step regression analysis, is it Pearson's Correlation Coefficient? If not, how would I calculate a sex-adjusted Pearson's Correlation Coefficient?







      python linear-regression pearson-correlation






      share|improve this question













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      asked Jan 3 at 21:47









      superasiantomtom95superasiantomtom95

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