加载中…Python替代SPSS进行各项统计检验
(2020-04-25 23:11:29)
标签：
python统计学 |
采用python的scipy库完成常用的假设检验, 配合pandas库非常好用
正态性检验
检验数据样本是否具有高斯分布。
from scipy.stats import shapiro
data = [21,12,12,23,19,13,20,17,14,19]
stat,p = shapiro(data)
print("stat为：%f" %stat,"p值为：%f" %p)
皮尔逊相关性检验
检查两个样本是否相关的统计检验
from scipy.stats import pearsonr
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
corr,p = pearsonr(data1,data2)
print("corr为：%f" %corr,"p值为：%f" %p)
卡方检验
检验两个分类变量的独立性
from scipy.stats import chi2_contingency
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p,dof,expected = chi2_contingency(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
T检验
检验两个独立样本的均值是否存在显著差异
from scipy.stats import ttest_ind
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = ttest_ind(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
配对T检验
检验两个配对样本的均值是否有显著差异
from scipy.stats import ttest_rel
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = ttest_rel(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
方差分析
测试两个或两个以上独立样本的均值是否存在显著差异
from scipy.stats import f_oneway
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = f_oneway(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
Mann-Whitney
检验两个独立样本的分布是否相等. 对应参数检验里面的t检验.
from scipy.stats import mannwhitneyu
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = mannwhitneyu(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
Wilcoxon符号秩检验
检验两个配对样本的分布是否均等. 对应参数检验里面的配对t检验.
from scipy.stats import wilcoxon
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = wilcoxon(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
Kruskal-wallis
H检验.
检验两个或多个独立样本的分布是否相等. 对应参数检验里面的方差分析.
from scipy.stats import kruskal
data1 = [21,12,12,23,19,13,20,17,14,19]
data2 = [12,11,8,9,10,15,16,17,10,16]
stat,p = kruskal(data1,data2)
print("stat为：%f" %stat,"p值为：%f" %p)
喜欢
0
赠金笔