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Linear and Non-linear Regression:Application to Competitor's Gasoline Volume Estimation

Zhai, Haomiao (2015) MASM01 20151
Mathematical Statistics
Abstract (Swedish)
This paper is dedicated to give a better estimation for competitor's
gasoline volume on the behalf of Kalibrate Technologies and also nd
a better way to improve business and sales performance. To achieve
this goal, both linear regression model and non-linear regression model
have been used. The comparison between dierent models is discussed
in the paper. The best model provides the most accurate prediction result
based on statistical criterion, whose accuracy is greatly enhanced
compared with existing models used in Kalibrate Technologies.
Please use this url to cite or link to this publication:
author
Zhai, Haomiao
supervisor
organization
course
MASM01 20151
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Multiple Linear Regression, Ridge Regression, LASSO, Partial Least Square Regression, Supporting Vector Regression, Random Forrest Regression
language
English
id
5034934
date added to LUP
2015-01-29 13:40:43
date last changed
2015-01-29 13:40:43
@misc{5034934,
  abstract     = {This paper is dedicated to give a better estimation for competitor's
gasoline volume on the behalf of Kalibrate Technologies and also nd
a better way to improve business and sales performance. To achieve
this goal, both linear regression model and non-linear regression model
have been used. The comparison between dierent models is discussed
in the paper. The best model provides the most accurate prediction result
based on statistical criterion, whose accuracy is greatly enhanced
compared with existing models used in Kalibrate Technologies.},
  author       = {Zhai, Haomiao},
  keyword      = {Multiple Linear Regression,Ridge Regression,LASSO,Partial Least Square Regression,Supporting Vector Regression,Random Forrest Regression},
  language     = {eng},
  note         = {Student Paper},
  title        = {Linear and Non-linear Regression:Application to Competitor's Gasoline Volume Estimation},
  year         = {2015},
}