eceisik

# Set formatting and styling options for the confusion matrices
title_size = 16
plt.rcParams.update({'font.size':16})
display_labels = ["Class 1", "Class 2", "Class 3", "Class 4"]  # Customize labels of the classes
colorbar = False
cmap = "Blues"  # Try "Greens". Change the color of the confusion matrix.
## Please see other alternatives at https://matplotlib.org/stable/tutorials/colors/colormaps.html
values_format = ".3f"  # Determine the number of decimal places to be displayed.

# Create subplots for given confusion matrices

eceisik

ttest,pvalue = stats.mannwhitneyu(test_team,developer_team, alternative="two-sided")
print("p-value:%.4f" % pvalue)
if pvalue <0.05:
    print("Reject null hypothesis")
else:
    print("Fail to recejt null hypothesis")

eceisik

check_normality(test_team)
check_normality(developer_team)
check_variance_homogeneity(test_team, developer_team)

eceisik

from scipy.stats import chi2
## calculate critical stat

alpha = 0.01
df = (5-1)*(2-1)
critical_stat = chi2.ppf((1-alpha), df)
print("critical stat:%.4f" % critical_stat)

eceisik

from scipy.stats import chi2_contingency

obs =np.array([[53, 23, 30, 36, 88],[71, 48, 51, 57, 203]])
chi2, p, dof, ex = chi2_contingency(obs, correction=False)

print("expected frequencies:\n ", np.round(ex,2))
print("degrees of freedom:", dof)
print("test stat :%.4f" % chi2)
print("p value:%.4f" % p)

eceisik

data = np.array([method_A, method_B, method_C]) 
posthoc_df=sp.posthoc_wilcoxon(data, p_adjust="holm")
# posthoc_df = sp.posthoc_nemenyi_friedman(data.T) ## another option for the posthoc test

group_names= ["Method A", "Method B","Method C"]
posthoc_df.columns= group_names
posthoc_df.index= group_names
posthoc_df.style.applymap(lambda x: "background-color:violet" if x<0.05 else "background-color: white")

eceisik

test_stat,p_value = stats.friedmanchisquare(method_A,method_B, method_C)
print("p value:%.4f" % p_value)
if p_value <0.05:
    print("Reject null hypothesis")
else:
    print("Fail to reject null hypothesis")
    
print(np.round(np.mean(method_A),2), np.round(np.mean(method_B),2), np.round(np.mean(method_C),2))

eceisik

check_normality(method_A)
check_normality(method_B)
check_normality(method_C)

print("p value:%.4f" % pvalue_levene)
if pvalue_levene <0.05:
    print("Reject null hypothesis >> The variances of the samples are different.")
else:
    print("Fail to reject null hypothesis >> The variances of the samples are same.")

eceisik

posthoc_df = sp.posthoc_mannwhitney([youtube,instagram, facebook], p_adjust = 'bonferroni')
group_names= ["youtube", "instagram","facebook"]
posthoc_df.columns= group_names
posthoc_df.index= group_names
posthoc_df.style.applymap(lambda x: "background-color:violet" if x<0.05 else "background-color: white")

eceisik

F, p_value = stats.kruskal(youtube, instagram, facebook)
print("p value:%.6f" % p_value)
if p_value <0.05:
    print("Reject null hypothesis")
else:
    print("Fail to reject null hypothesis")