Multiple Regression

A. Introduction

Multiple regression is a development of simple regression. If simple regression has one dependent variable (Y) and one independent variable (X), then multiple regression has one dependent variable (Y) and 2 independent variables (X1, X2). Multiple regression analysis is a statistical analysis to predict the influence value of 2 or more independent variables on 1 dependent variable, to prove whether there is a causal relationship between 2 or more independent variables and one dependent variable or dependent variable.

The multiple regression equation implies that in a regression equation there is one dependent variable and more than one independent variable. Regression analysis was used to determine the effect of the independent variables X1, X2, X3, X4, X5, X6 on the dependent variable Y. Multiple regression in lisrel has assumptions that must be met, namely normality and multicollinearity (Ghozali, 2008: 36). The most fundamental assumption in multivariate analysis is normality, which is the form of a distribution of data on a single metric variable to produce a normal distribution. A data distribution that does not form a normal distribution, then the data is not normal, otherwise the data is said to be normal if it forms a normal distribution. If the normality assumption is not met and the normality deviation is large, then all statistical test results are invalid because t-test calculations and so on, are calculated with normal data assumptions.