SPSS Multiple Linear Regression Analysis and Classical Assumption Test for Questionnaire Data.
In this video, we explain and practice how to do SPSS multiple linear regression analysis along with the Classical Assumption Test for questionnaire data or primary 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 analysis aims to determine whether or not there is an influence of independent variables on the dependent variable, where the minimum use of independent variables is two variables.
However, before carrying out the multiple linear regression test, there is a classical assumption test or prerequisite test that must be met. There are 3 statistical tests used in the classical assumption test of multiple linear regression for questionnaire data, namely the SPSS Multicollinearity test, the SPSS normality 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 SPSS simultaneous F test and the SPSS partial t test or usually called the SPSS hypothesis test.
Apart from these 3 statistical tests, this video also shows how to find multiple linear regression equations and how to read the results of multiple linear regression analysis.
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.