2026-06-25T03:20:42Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00008471 2024-03-01T06:14:27Z 1582963436320:1707206634013

The Violation for assumptions of multiple regression model (Ma May Thu, 2019) May Thu The study intends to apply some of the most common and appropriate detections and remedies methods to meet the assumptions of a multiple linear regression model. When the assumptions are violated, then the inferences about the parameter estimate will be incorrect. The secondary data for maize (1998-2018), wheat (1998-2018), rice (1966-2018) and sesame (1989-2018) of Myanmar. Maize data for linearity assumption is used to detect and remedy. Wheat data for normality assumption is used to apply in the detection and remedial ways. Rice data for homoscedasticity assumption is used and sesame data for micronumerosity assumption, multicollinearity assumption, and the nature of independent variables assumption, autocorrelation assumption are used to diagnosis and resolving ways. 2019-11-01 https://meral.edu.mm/records/8471