A comparison among Different Methods for Estimating Regression Parameters with Autocorrelation Problem under Exponentially Distributed Error

Abstract

Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of cigarettes according to the US Federal Trade Commission.

Multiple Linear Regression and Deep Learning in Body Temperature Detection and Mask Detection

“…The relationship between these variables is called a linear regression model. Based on the use of independent variables, linear regression is divided into two categories, namely linear regression and multiple linear regression [16]. To use this method, the researcher uses 2 independent variables, namely temperature data from thermal cam sensor readings and distance data from ultrasonic sensor readings, and then uses 1 related variable, namely temperature data generated using a thermogenic.…”