一个坐标轴区域为四边形，四个顶点坐标分别为（81，104.8）、（0，98.8）、（0，247）、（180，215），请给出matlab画出这个图并给图形中的区域配色

代码如下：

% 定义四个顶点坐标
x = [81, 0, 0, 180];
y = [104.8, 98.8, 247, 215];

% 绘制四边形
patch(x, y, 'r');

% 设置坐标轴范围和标题
xlim([-20, 200]);
ylim([80, 260]);
title('四边形区域');

运行结果如下：


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java建一个string类型的数组并填入数据：0.15,0.7,1.52,2.65,4.08,6.06,7.98,9.91,11.86,14.05,15.9,17.84,20.06,21.99,23.94,25.9,27.84,30.06,31.97,33.9,35.61,37.03,38.26,38.99,39.42,39.53,39.53,39.77,40.24,40.8,41.28,41.77,42.3,42.78,43.27,43.75,44.3,44.78,45.27,45.76,46.31,46.79,47.27,47.75,48.31,48.79,49.27,49.76,50.3,50.78,51.27,51.76,52.31,52.8,53.28,53.77,54.25,54.74,55.3,55.78,56.27,56.75,57.3,57.79,58.28,58.76,59.25,59.81,60.29,60.78,61.26,61.75,62.3,62.78,63.27,63.75,64.31,64.79,65.28,65.76,66.24,66.8,67.28,67.76,68.25,68.81,69.28,69.77,70.25,70.81,71.29,71.78,72.26,72.75,73.3,73.79,74.25,74.8,75.29,75.77,76.24,76.8,77.28,77.77,78.25,78.81,79.29,79.78,80.26,80.75,81.31,81.79,82.27,82.76,83.3,83.79,84.27,84.76,85.25,85.8,86.29,86.77,87.26,87.74,88.3,88.78,89.27,89.76,90.31,90.79,91.28,91.76,92.31,92.8,93.28,93.77,94.25,94.81,95.29,95.77,96.25,96.8,97.28,97.77,98.31,98.8,99.28,99.77,100.25,100.8,101.29,101.77,102.26,102.79,103.28,103.76,104.24,104.8,105.28,105.77,106.25,106.81,107.28,107.77,108.26,108.81,109.29,109.77,110.25,110.8,111.28,111.77,112.26,112.81,113.29,113.78,114.26,114.75,115.3,115.78,116.27,116.75,117.3,117.79,118.27,118.76,119.31,119.79,120.28,120.76,121.31,121.8,122.28,122.76,123.31,123.79,124.28,124.76,125.24,125.8,126.28,126.77,127.25,127.8,128.28,128.77,129.25,129.8,130.27,130.75,131.24,131.8,132.28,132.76,133.25,133.8,134.28,134.77,135.26,135.81,136.3,136.78,137.26,137.75,138.3,138.79,139.27,139.76,140.25,140.8,141.29,141.78,142.26,142.75,143.3,143.78,144.26,144.81,145.28,145.76,146.3,146.77,147.25,147.8,148.28,148.76,149.25,149.8,150.29,150.76,151.31,151.79,152.26,152.81,153.28,153.69,

String[] arr = {"0.15","0.7","1.52","2.65","4.08","6.06","7.98","9.91","11.86","14.05","15.9","17.84","20.06","21.99","23.94","25.9","27.84","30.06","31.97","33.9","35.61","37.03","38.26","38.99","39.42","39.53","39.53","39.77","40.24","40.8","41.28","41.77","42.3","42.78","43.27","43.75","44.3","44.78","45.27","45.76","46.31","46.79","47.27","47.75","48.31","48.79","49.27","49.76","50.3","50.78","51.27","51.76","52.31","52.8","53.28","53.77","54.25","54.74","55.3","55.78","56.27","56.75","57.3","57.79","58.28","58.76","59.25","59.81","60.29","60.78","61.26","61.75","62.3","62.78","63.27","63.75","64.31","64.79","65.28","65.76","66.24","66.8","67.28","67.76","68.25","68.81","69.28","69.77","70.25","70.81","71.29","71.78","72.26","72.75","73.3","73.79","74.25","74.8","75.29","75.77","76.24","76.8","77.28","77.77","78.25","78.81","79.29","79.78","80.26","80.75","81.31","81.79","82.27","82.76","83.3","83.79","84.27","84.76","85.25","85.8","86.29","86.77","87.26","87.74","88.3","88.78","89.27","89.76","90.31","90.79","91.28","91.76","92.31","92.8","93.28","93.77","94.25","94.81","95.29","95.77","96.25","96.8","97.28","97.77","98.31","98.8","99.28","99.77","100.25","100.8","101.29","101.77","102.26","102.79","103.28","103.76","104.24","104.8","105.28","105.77","106.25","106.81","107.28","107.77","108.26","108.81","109.29","109.77","110.25","110.8","111.28","111.77","112.26","112.81","113.29","113.78","114.26","114.75","115.3","115.78","116.27","116.75","117.3","117.79","118.27","118.76","119.31","119.79","120.28","120.76","121.31","121.8","122.28","122.76","123.31","123.79","124.28","124.76","125.24","125.8","126.28","126.77","127.25","127.8","128.28","128.77","129.25","129.8","130.27","130.75","131.24","131.8","132.28","132.76","133.25","133.8","134.28","134.77","135.26","135.81","136.3","136.78","137.26","137.75","138.3","138.79","139.27","139.76","140.25","140.8","141.29","141.78","142.26","142.75","143.3","143.78","144.26","144.81","145.28","145.76","146.3","146.77","147.25","147.8","148.28","148.76","149.25","149.8","150.29","150.76","151.31","151.79","152.26","152.81","153.28","153.69"};

表1 2022年居民消费价格比上年涨跌幅度 指 标 价格指数(上年=100) 比上年涨跌幅度（%） 居民消费价格 102.3 2.3 食品烟酒 102.4 2.4 其中：粮食 99.6 -0.4 食用油 107.0 7.0 鲜菜 98.8 -1.2 畜肉类 90.4 -9.6 禽肉类 105.6 5.6 水产品 105.7 5.7 蛋类 107.9 7.9 衣着 99.1 -0.9 居住 100.5 0.5 生活用品及服务 101.3 1.3 交通通信 106.5 6.5 教育文化娱乐 102.6 2.6 医疗保健 101.1 1.1 其他用品及服务 101.0 1.0 问题2. 根据表1，建立适当的数学模型，探讨各类消费品指标对全年CPI涨幅情况的影响，并检验其结果。使用python语言实现，使用多元线性回归分析，并每一行给出代码注释

为了建立适当的数学模型，我们需要进行多元线性回归分析。以全年CPI涨幅情况为因变量，各类消费品指标作为自变量，建立如下模型：

CPI涨跌幅度（%） = β0 + β1 * 食品烟酒 + β2 * 衣着 + β3 * 居住 + β4 * 生活用品及服务 + β5 * 交通通信 + β6 * 教育文化娱乐 + β7 * 医疗保健 + β8 * 其他用品及服务

其中，β0 为截距，β1~β8 分别为各个自变量的系数。

使用Python进行多元线性回归分析的具体实现步骤如下：

Step 1: 导入必要的库和数据

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# 读入数据
data = pd.read_csv('data.csv')

Step 2: 提取自变量和因变量

X = data[['食品烟酒', '衣着', '居住', '生活用品及服务', '交通通信', '教育文化娱乐', '医疗保健', '其他用品及服务']]
y = data['比上年涨跌幅度（%）']

Step 3: 建立线性回归模型并拟合数据

model = LinearRegression()
model.fit(X, y)

Step 4: 输出模型的系数和截距

print('系数:', model.coef_)
print('截距:', model.intercept_)

Step 5: 进行模型的预测并计算误差

y_pred = model.predict(X)
error = y - y_pred
print('误差:', error)

Step 6: 检验模型结果

通过计算误差，我们可以评估模型的拟合效果。误差越小，说明模型的预测结果越准确。

另外，我们还可以通过交叉验证等方法对模型进行进一步的验证和优化。

完整的代码如下：

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# 读入数据
data = pd.read_csv('data.csv')

# 提取自变量和因变量
X = data[['食品烟酒', '衣着', '居住', '生活用品及服务', '交通通信', '教育文化娱乐', '医疗保健', '其他用品及服务']]
y = data['比上年涨跌幅度（%）']

# 建立线性回归模型并拟合数据
model = LinearRegression()
model.fit(X, y)

# 输出模型的系数和截距
print('系数:', model.coef_)
print('截距:', model.intercept_)

# 进行模型的预测并计算误差
y_pred = model.predict(X)
error = y - y_pred
print('误差:', error)