
# before <-  c(16,15,13,12,18,19,20,11,12,15,9,22)
# after <- c(12,12,12,12,12,12,12,12,12,12,12,12)
 before <- c(25.7,24.4,21.1,25.2,26.4,23.8,21.5,22.9,23.1)
after <- c(22.5,23.2,20.6,23.4,25.4,20.4,20.6,21.9,22.6)

difference <- diff(cbind(before, after))
print(difference)


# 执行配对样本t检验
result <- t.test(before, after, paired = TRUE)

# 输出检验结果
print(result)


# 执行独立样本t检验
result <- t.test(before, after)

# 输出检验结果
print(result)





x <- c(0.408,0.198,0.700,0.260,0.224,0.183,0.114,0.180,0.176,0.263,0.270,0.182,0.247,0.252,0.173,0.160,0.247,0.215,0.223,0.223,0.207,0.257,0.447,0.201,0.208,0.198,0.161,0.160,0.140,0.134,0.140,0.160,0.148,0.150,0.198,0.204,0.166,0.160,0.164,0.154,0.198,0.253,0.190,0.134,0.132,0.128,0.163	)

# 计算平均值\中位数、标准差，标准误
mean_x <- mean(x)

# 计算标准差
sd_x <- sd(x)
#中位数
result.median <- median(x)
# 计算标准误差
#se_x <- std.error(x)


# 计算变异系数
cv <- (sd_x / mean_x) * 100

# 打印结果


cat("中位数:", result.median)
cat("平均值:", mean_x)
cat("标准差:", sd_x)
cat("变异系数:", cv, "%")





data <- c(16,15,13,12,18,19,20,11,12,15,9,22) # 生成包含100个正态分布随机数的向量
 
# 计算数据的平均值
avg_value <- mean(data)
 
# 将数据按照与平均值的大小关系进行标记（>、<或=）
labels <- ifelse(data > avg_value, ">", ifelse(data < avg_value, "<", "="))
 
# 输出结果

print(data)
print("\n平均值：")
print(avg_value)
print("\n与平均值的差异情况：")
print(labels)