#pearsoncorrelation

Quantify the strength and direction of linear relationships with the Pearson correlation coefficient (r), which ranges from −1 (perfect inverse correlation) to +1 (perfect positive correlation). Widely used in fields like finance, healthcare, and social sciences, it offers a clear, standardized metric for understanding variable associations—but it only captures linear relationships and is sensitive Read More...

The article “Analyzing Data? Here’s Why You Need the Pearson Correlation Coefficient” explains how this statistical metric helps measure the strength and direction of the relationship between two variables. It's essential for uncovering patterns, validating assumptions, and enhancing the accuracy of data analysis in fields like finance, healthcare, and marketing Read More...

Just ran a Pearson correlation between total personal income (S1Q10A) and number of children under 18 in the household (CHLD0_17) using the NESARC dataset. The result?

r = -0.016 p = 0.0009

💥 Yes, it's statistically significant. 😅 But practically? Not so much.

This teeny-tiny negative correlation means higher income is slightly linked to fewer kids—but the effect is so small it’s basically a shrug from the universe. 🤷‍♂️📉

In short: Money ≠ more (or fewer) children — at least not in any meaningful way.

in case you want to try out the code:

import pandas as pd import numpy as np import seaborn as sns import scipy.stats import matplotlib.pyplot as plt

Load the data

data = pd.read_csv('nesarc.csv', low_memory=False)

Convert to numeric properly

data['CHLD0_17'] = pd.to_numeric(data['CHLD0_17'], errors='coerce') data['S1Q10A'] = pd.to_numeric(data['S1Q10A'], errors='coerce')

Drop rows with NA in either of the two columns

data_clean = data[['S1Q10A', 'CHLD0_17']].dropna()

Scatterplot

sns.regplot(x="S1Q10A", y="CHLD0_17", fit_reg=True, data=data_clean) plt.xlabel('Total Personal Income (USD)') plt.ylabel('Number of Children Under 18 in Household') plt.title('Scatterplot: Income vs. Number of Children') plt.show()

Pearson correlation

bys dvlpstatus: pwcorr femaleemployrate compall, sig

-> dvlpstatus = 0

                       | female~e  compall

------------+------------------

femaleempl~e |   1.0000 

                       |

                       |

         compall |  -0.3782   1.0000 

                       |   0.0001

                       |

--------------------------------------------------------------------

-> dvlpstatus = 1

                       | female~e  compall

------------+------------------

femaleempl~e |   1.0000 

                       |

                       |

         compall |  -0.0437   1.0000 

                       |   0.8509

                       |

I ran two Pearson correlation tests, one examining the association between female employment rate and primary schooling completion rate for developing countries (dvlpstatus==0), and one examining the same association for developed countries (dvlpstatus==1).

The results indicate that there is a stronger negative relationship in developing countries than developed countries between the two quantitative variables, which is suggested through the correlation coefficient (r) for developing countries being closer to -1 than for developed countries (r = -0.3782 and r = -0.0437 respectively). Furthermore, for developed countries, p = 0.8509 >0.05, showing that the relationship between female employment rate and primary schooling completion rate is not statistically significant.

use "P:\QAC\qac201\STUDENTS\section4\yhorng\project\data managed final.dta" 

//effect of female employment rate on primary schooling completion rate

pwcorr femaleemployrate compall, sig

                        | female~e  compall

-----------------+-------------------

femaleempl~e |   1.0000 

                        |

                        |

          compall |  -0.2933   1.0000 

                        |   0.0011

                        |

Thus, r = -0.2933 and p = 0.0011 (p<0.05)

Among the 204 countries (my sample), the correlation coefficient

between the quantitative explanatory variable (female employment rate) and the quantitative response variable (completion rate) was -0.2933, suggesting that only 0.086% (i.e. 

-0.2933 squared) of the variance in the 2007 primary schooling completion rates can be explained by the rate of female employment in the country.  

  //I've also decided to create a scatter plot to illustrate this data

scatter compall femaleemployrate