Linear Regression analysis is a statistical analysis method predicting the relationship between the independent and dependent variables. Actually many other analytical methods that can to measure the relationship between variables, but the regression analysis focuses on the relationship between the dependent variable (dependent variable) with independent variable (independent variable). In particular, regression analysis helps researchers to determine changes in the dependent variable caused by the independent variable, where other variables is constant.
Linear Regression analysis includes in group of causality analysis. In causality relationship, one variable affects other variables. independent variable is random, while dependent variable is fix. Regression analysis is different with the correlation analysis where no variable that becomes the cause variable to another variable. Therefore, in statistics, the correlation between the two variables in the analysis are constant.
Theoretically, correlation analysis figures the relationship between variables that does not have a causal relationship. Both of these variables are related only by chance. For example, the relationship between weight and height’s student. The body weight and height is probably related but the weight, certainly not cause the height or vice verca. The relationship pattern is not the same as pattern on causal relationship. The effect of certain drugs against diseases is a good sample for causality relationship.
In Linear Regression analysis, we can involve one independent variable and one dependent variable, which is commonly called a simple regression analysis. We can also involve more than one independent variables with the dependent variable which is commonly called multiple regression analysis.
Linear Regression Analysis Formula
Simply, Regression has formula as follows:
Y = a+bx+e
Y represents the independent variable. it is the response variable that will change when the x variable changes. a represents a constant/ intercept. It is a basic value that is not influenced by X variabel. Meanwhile, b represents the regression coefficient. It shows the influence value of x variabel to y. e for error is the gap between population model and sample model.