for our scenariothree data subsets or groups from the data are created based on wine quality ratings the mean values in the first test would be based on the wine alcohol content and the second test would be based on the wine ph levels also let' assume the null hypothesis is that the group means for lowmediumand high quality wine is same and the alternate hypothesis would be that there is difference (statistically significantbetween at least two group means the following snippet helps us perform the one-way anova test in [ ]from scipy import stats fp stats f_oneway(wines[wines['quality_label'='low']['alcohol']wines[wines['quality_label'='medium']['alcohol']wines[wines['quality_label'='high']['alcohol']print('anova test for mean alcohol levels across wine samples with different quality ratings'print(' statistic:' '\tp-value:'pfp stats f_oneway(wines[wines['quality_label'='low']['ph']wines[wines['quality_label'='medium']['ph']wines[wines['quality_label'='high']['ph']print('\nanova test for mean ph levels across wine samples with different quality ratings'print(' statistic:' '\tp-value:'panova test for mean alcohol levels across wine samples with different quality ratings statistic -value - anova test for mean ph levels across wine samples with different quality ratings statistic -value from the preceding results we can clearly see we have -value much less than in the first test and greater than in the second test this tells us that there is statistically significant difference in alcohol level means for at least two groups out of the three (rejecting the null hypothesis in favor of the alternativehoweverin case of ph level meanswe do not reject the null hypothesis and thus we conclude that the ph level means across the three groups are not statistically significantly different we can even visualize these two features and observe the means using the following snippet (ax ax plt subplots( figsize=( ) suptitle('wine quality alcohol content/ph'fontsize= subplots_adjust(top= wspace= sns boxplot( ="quality_label" ="alcohol"data=winesax=ax ax set_xlabel("wine quality class",size ,alpha= ax set_ylabel("wine alcohol %",size ,alpha= sns boxplot( ="quality_label" ="ph"data=winesax=ax ax set_xlabel("wine quality class",size ,alpha= ax set_ylabel("wine ph",size ,alpha=