Anova, Correlation, Regression

Agnieszka

Anova

import scipy.stats as scs
import pandas as pd
#ANOVA
#NON-PARAMETRIC
FileData = pd.read_csv('man_and_woman.csv', sep=',', na_values='.')
KruskalWallis_results = scs.kruskal(FileData['Age'], FileData['IQ'], FileData['PIQ'])
print(KruskalWallis_results)

Friedmann_results = scs.friedmanchisquare(FileData['Age'], FileData['IQ'], FileData['PIQ'])
print(Friedmann_results)

#POST-HOC
ANOVA_results = scs.f_oneway(FileData['Age'], FileData['IQ'], FileData['PIQ'])
print(ANOVA_results)

Results:
KruskalResult(statistic=67.47559460718044, pvalue=2.227721003370935e-15)
FriedmanchisquareResult(statistic=43.73584905660375, pvalue=3.1833254890177335e-10)
F_onewayResult(statistic=92.45047895919548, pvalue=8.256579887742929e-25)

Correlation & Regression

#CORRELATION
#Correlation coefficient
CC_results = scs.pearsonr(FileData['IQ'], FileData['PIQ'])
print("Cor.coefficient:", CC_results)

#Spearman rank coefficient
Spearmann_results = scs.spearmanr(FileData['IQ'], FileData['PIQ'])
print(Spearmann_results)

#Regression
Regression_results = scs.linregress(FileData['IQ'], FileData['PIQ'])
print(Regression_results)

Results:
Cor.coefficient: (0.778135113690377, 3.4381859970690258e-09)
SpearmanrResult(correlation=0.7149801806441931, pvalue=2.1806977447399098e-07)
LinregressResult(slope=0.7404062323284799, intercept=27.84035979789529, rvalue=0.7781351136903771, pvalue=3.438185997069003e-09, stderr=0.09695047023723001)