• In case of simple linear regression with one variable we interpret slop coefficient as follows
  • y = b0*x0 + c
  • b0 is increase in y for unit increase in x0
• In case of multiple regression:
  • y = b0*x0 + b1*x1 + c
  • b0 is increase in y for unit increase in x0 keeping x1 constant

 

Implication of keeping other variable constant:

• Consider
  • house_price = b0 * no_of_bedrooms + c       ….. (1)
  • house_price = b0 * no_of_bedrooms + b1*square_feet + c     …..(2)
• In (1) there are high chances that b0 will be positive
• In (2) it can be negative
  • If you increase no of bedrooms keeping square feet constant each room will be smaller
  • This may decrease house price

 

 

Reference :

Coursera course on regression by University of Washington DC : https://www.coursera.org/specializations/machine-learning