SPSS Multiple Linear Regression Analysis and Classical Assumptions Test for Secondary Data.
In this video, we explain and practice how to do SPSS multiple linear regression analysis along with the Classical Assumption Test for secondary data. Apart from explaining the basic concepts of multiple linear regression and the classical assumption test, this video will also include the testing criteria for each statistical test used and the interpretation of the output.
Multiple linear regression aims to determine whether or not there is an influence of the independent variables on the dependent variable, where the minimum number of independent variables is two..
However, before carrying out the multiple linear regression test, there is a classical assumption test or prerequisite test that must be met. There are four statistical tests used in the classical assumption test of multiple linear regression for secondary data, namely the SPSS Multicollinearity test, the SPSS normality test, the SPSS autocorrelation test, and the SPSS heteroscedasticity test.
If the Classical Assumption Test has been fulfilled, then it can proceed to multiple linear regression analysis, where there are 3 statistical tests used, namely the SPSS coefficient of determination, the t test, and the f test with SPSS or usually called the SPSS hypothesis test.
Watch until the end, my friends, this video discusses the Multiple Linear Regression Tutorial with SPSS and how to test the classical assumptions of multiple linear regression in SPSS.