import scipy.stats as stats

# 生成兩組樣本數(shù)據(jù)
data1 = [25, 30, 35, 40, 45]
data2 = [30, 32, 35, 38, 42]

# 執(zhí)行獨(dú)立樣本 t 檢驗(yàn)
t_stat, p_value = stats.ttest_ind(data1, data2)
print("t 統(tǒng)計(jì)量:", t_stat)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建觀察頻數(shù)表
observed = [[30, 15], [25, 20]]

# 執(zhí)行卡方檢驗(yàn)
chi2, p_value, _, _ = stats.chi2_contingency(observed)
print("卡方統(tǒng)計(jì)量:", chi2)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建兩組連續(xù)變量數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 6]

# 計(jì)算 Pearson 相關(guān)系數(shù)
correlation, _ = stats.pearsonr(x, y)
print("Pearson 相關(guān)系數(shù):", correlation)

if abs(correlation) > 0.7:
    print("線性相關(guān)性強(qiáng)")
else:
    print("線性相關(guān)性弱")

import scipy.stats as stats

# 單樣本數(shù)據(jù)
data = [28, 30, 29, 32, 31]

# 已知均值
known_mean = 30

# 執(zhí)行單樣本 t 檢驗(yàn)
t_stat, p_value = stats.ttest_1samp(data, known_mean)
print("t 統(tǒng)計(jì)量:", t

_stat)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建兩組配對樣本數(shù)據(jù)
before = [28, 30, 29, 32, 31]
after = [27, 29, 28, 31, 30]

# 執(zhí)行 Wilcoxon 符號(hào)秩檢驗(yàn)
w_stat, p_value = stats.wilcoxon(before, after)
print("Wilcoxon 符號(hào)秩統(tǒng)計(jì)量:", w_stat)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建多組樣本數(shù)據(jù)
group1 = [65, 68, 72, 70, 74]
group2 = [58, 63, 65, 61, 59]
group3 = [72, 70, 75, 71, 73]

# 執(zhí)行單因素 ANOVA
f_stat, p_value = stats.f_oneway(group1, group2, group3)
print("F 統(tǒng)計(jì)量:", f_stat)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建兩組數(shù)據(jù)
data1 = [0.1, 0.2, 0.3, 0.4, 0.5]
data2 = [0.2, 0.3, 0.4, 0.5, 0.6]

# 執(zhí)行 Kolmogorov-Smirnov 檢驗(yàn)
ks_stat, p_value = stats.ks_2samp(data1, data2)
print("KS 統(tǒng)計(jì)量:", ks_stat)
print("p 值:", p_value)

if p_value < 0.05:
    print("差異顯著")
else:
    print("差異不顯著")

import scipy.stats as stats

# 創(chuàng)建樣本數(shù)據(jù)
data = [2.3, 3.```python
7, 4.5, 6.8, 5.1, 8.2]

# 執(zhí)行 Shapiro-Wilk 正態(tài)性檢驗(yàn)
statistic, p_value = stats.shapiro(data)
print("Shapiro-Wilk 統(tǒng)計(jì)量:", statistic)
print("p 值:", p_value)

if p_value < 0.05:
    print("不符合正態(tài)分布")
else:
    print("符合正態(tài)分布")

import scipy.stats as stats

# 創(chuàng)建自變量和因變量數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 6]

# 執(zhí)行線性回歸分析
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print("斜率:", slope)
print("截距:", intercept)
print("相關(guān)系數(shù):", r_value)
print("p 值:", p_value)

if p_value < 0.05:
    print("回歸模型顯著")
else:
    print("回歸模型不顯著")

import statsmodels.api as sm

# 創(chuàng)建自變量和因變量數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 6]

# 添加常數(shù)項(xiàng)
x = sm.add_constant(x)

# 執(zhí)行分位數(shù)回歸分析
quantiles = [0.25, 0.5, 0.75]
for q in quantiles:
    model = sm.QuantReg(y, x)
    result = model.fit(q=q)
    print(f"分位數(shù) {q}:")
    print(result.summary())