## COMPARISON OF RESAMPLING METHODS IN MULTIPLE LINEAR REGRESSION

JOURNAL OF SCIENCE AND ARTS, no.1, pp.91-104, 2019 (ESCI)

• Publication Type: Article / Article
• Publication Date: 2019
• Journal Name:
• Journal Indexes: Emerging Sources Citation Index (ESCI)
• Page Numbers: pp.91-104
• Keywords: Bootstrap method, jackknife method, multiple linear regressions, generalization, BOOTSTRAP, JACKKNIFE, BIAS
• Ondokuz Mayıs University Affiliated: Yes

#### Abstract

In order to estimate model parameters in multiple regression models, resampling methods of bootstrap and jackknife are used. Resampling methods are used as an alternative readjustment method to the least squares method (OLS) especially when assumptions belonging to error term in regression analysis are not met. Data used in the study are taken from 25 advertisements in Sahibinden.com website and the price of beetle car brand is accepted as dependent variable for multiple linear regression models. It is aimed that price variable taken is tried to be explained with the help of variables of fuel, case type, salesman, sunroof wind shield, upholstery, age and engine size. When we examined the variables, it is seen that categorical variables are in question and dummy variable must be used. Firstly, model parameters of this obtained data are estimated using OLS and significances of parameters are tested, then, model parameters, significances of estimated parameters, coefficient of determination, and standard error of the model and % 90 confidence intervals are estimated using one of the resampling methods, bootstrap and jackknife method and results belonging to these three methods are compared. Also, generalization condition, which is to the population, of parameter estimation results belonging to explanatory variables used in this study are reviewed with the help of jackknife resampling method ye It has been seen that the salesman and upholstery independent variables have a considerable effect at a significance level of .10 on the dependent variable of price dependence of decision making (p < .10) and Jackknife have confirmed these generalization.