偏相关分析在python上的实现

偏相关分析在python上的实现 数据 年份ndvi平均气温降水量相对湿度19880.491318.345229148.556.719890.447217.53081599.355.419900.441217.84865122.454.219910.458618.07474888.953.519920.364218.72435136.052.819930.410417.8901103.654.919940.466918.140145118.357.019950.501018.582622121.154.419960.474918.49037149.551.719970.453918.03417567.953.219980.394818.110134150.254.719990.496718.608214118.553.320000.475918.054574103.551.820010.474617.962316127.452.520020.504018.438706129.453.220030.498318.789157118.254.420040.496218.601598117.555.620050.529918.465439131.554.620060.478018.68936488.353.720070.501418.998927162.851.820080.518918.75575136.450.020090.513018.29560697.750.920100.513518.671832121.651.820110.531219.048781100.352.720120.497618.974479163.653.620130.655118.79582293.050.520140.633519.526067151.547.520150.613219.257401141.049.920160.650418.67825288.852.420170.639719.007982153.051.620180.631419.358269143.651.8 需求 

控制降水量、相对湿度变量，研究ndvi与平均气温变量的关系。

结果 nrCI95%p-valpearson310.568398[0.26 0.77]0.001296

可以看到，ndvi与平均气温的偏相关系数为0.568，是一个很小的正相关。随着平均温度的增加。如果当前的降水量和相对湿度保持不变，ndvi也会增加。

python代码