NX総合研究所 Official site

Basic Edition: Analyzing Logistics Data with Python and Statistics

A detailed introduction in blog format about multiple regression analysis and the comparison of observed values and predicted values!

In this blog, I would like to incorporate a more practical approach (such as correlation and multiple regression analysis) to advance the learning of Python and statistics. I will output the correlation between variables from a dataset read using pandas' read.csv() in a heatmap. The value of the correlation coefficient can range from -1 to 1, where a value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates a weak correlation. *For more details about the blog, you can view it through the related links. Please feel free to contact us for more information.*

Related Link - https://www.nx-soken.co.jp/topics/logistics-2402-0…

basic information

*You can view the detailed content of the blog through the related links. For more information, please feel free to contact us.*

Price range

Delivery Time

Applications/Examples of results

*You can view the detailed content of the blog through the related links. For more information, please feel free to contact us.*

Warehouse Operation Analysis Tool "Rojitan"

PRODUCT

Work Time Measurement Tool "Jobtan"

PRODUCT

Recommended products

Distributors