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Use the fastfood.csv file to complete the following assignment. Create a file, fastfood.py, that loads the .csv file and runs a regression predicting calories from total_fat, sat_fat, cholesterol, and sodium, in that order. Add a constant using sm.add_constant(data). ● Note: you will not need to upload the .csv to CodeGrade because I have pre-loaded it, but you will need to read in the data. ● Then, print the following to two decimals print(model.mse_total.round(2)) print(model.rsquared.round(2)) print(model.params.round(2)) print(model.pvalues.round(2))

Use the fastfood.csv file to complete the following assignment. Create a file, fastfood.py, that loads the .csv file and runs a regression predicting calories from total_fat, sat_fat, cholesterol, and sodium, in that order. Add a constant using sm.add_constant(data). ● Note: you will not need to upload the .csv to CodeGrade because I have pre-loaded it, but you will need to read in the data. ● Then, print the following to two decimals print(model.mse_total.round(2)) print(model.rsquared.round(2)) print(model.params.round(2)) print(model.pvalues.round(2))

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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fastfood.py -> using "sys.argv"

● Use the fastfood.csv file to complete the following assignment. Create a file, fastfood.py, that loads the .csv file and runs a regression predicting calories from total_fat, sat_fat, cholesterol, and sodium, in that order. Add a constant using sm.add_constant(data). ● Note: you will not need to upload the .csv to CodeGrade because I have pre-loaded it, but you will need to read in the data. ● Then, print the following to two decimals print(model.mse_total.round(2)) print(model.rsquared.round(2)) print(model.params.round(2)) print(model.pvalues.round(2))

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