如何对对每个站点城市的发货量、收货量、总快递数量进行统计和分析,计算增长/减少趋势和相关性。
时间: 2024-03-27 19:39:52 浏览: 21
对于每个站点城市的发货量、收货量、总快递数量的统计和分析,可以采用MATLAB中的数据分析工具箱中的函数进行处理,例如:
1. 统计每个站点城市的发货量、收货量、总快递数量
```matlab
% 导入数据
data = readtable('data.csv');
% 统计每个站点城市的发货量、收货量、总快递数量
shipment = table;
shipment.city = unique(data.city);
shipment.shipment_in = accumarray(data.city_id, data.shipment_in);
shipment.shipment_out = accumarray(data.city_id, data.shipment_out);
shipment.total_shipment = shipment.shipment_in + shipment.shipment_out;
```
2. 计算增长/减少趋势和相关性
```matlab
% 计算每个站点城市的发货量、收货量、总快递数量的增长/减少趋势和相关性
t = 1:size(shipment, 1);
% 发货量增长/减少趋势
p_shipment_in = polyfit(t, shipment.shipment_in, 1);
shipment_in_trend = p_shipment_in(1);
shipment_in_corr = corr(shipment.shipment_in(1:end-1), shipment.shipment_in(2:end));
% 收货量增长/减少趋势
p_shipment_out = polyfit(t, shipment.shipment_out, 1);
shipment_out_trend = p_shipment_out(1);
shipment_out_corr = corr(shipment.shipment_out(1:end-1), shipment.shipment_out(2:end));
% 总快递数量增长/减少趋势
p_total_shipment = polyfit(t, shipment.total_shipment, 1);
total_shipment_trend = p_total_shipment(1);
total_shipment_corr = corr(shipment.total_shipment(1:end-1), shipment.total_shipment(2:end));
```
在上述代码中,我们使用了MATLAB中的polyfit函数计算线性回归模型的系数,使用了MATLAB中的corr函数计算相关系数。通过这些分析,可以对每个站点城市的发货量、收货量、总快递数量的增长/减少趋势和相关性进行评估。
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