Assalamualaikum warahmatullahi wabarakatuh Good evening, my friends. On this occasion, I will teach material about multiple linear regression analysis using the SPSS program. Before I do practice in the SPSS program. I want to say thank for my friends who have given gift or coffee money through one of YouTube's features called super thanks. Immediately, here are the basic concepts in multiple regression. Regression analysis aims to determine whether or not there is an influence of the independent variables on the dependent variable. There are several kinds of regression analysis, including multiple linear regression analysis. Multiple regression is used when the number of independent variables is at least two variables. Before we do multiple regression analysis. There are several prerequisite tests that must be fulfilled, including passing the normality test, passing the heteroscedasticity test, passing the multicollinearity test and passing the autocorrelation test. For the autocorrelation test, it is used when the data pattern is a time series or for secondary data. The data scale used in multiple regression, namely interval or ratio data, especially on the dependent variable or Y variable. Immediately, we can go to the SPSS program. Here I use IBM SPSS version 23. To perform multiple regression analysis, we can select the analyze menu, then select regression, then select linear. Enter variable Y into the dependent column, then variable X1 to variable X5 into the independent column, then select ok. Here already appears SPSS output. For the interpretation of the multiple regression test output, all of you can focus on the PDF file. Here there is an analysis of the SPSS output for multiple linear regression in the first table, namely the model summary table. Where this table shows the test of the coefficient of determination. It is known that the adjusted r-square value is 0.360, this value, my friends. So it is concluded that the contribution of the influence of the independent variables on the dependent variable simultaneously or jointly is 36%. Furthermore, for the ANOVA table or F test. The regression model is declared fit, if the significant value obtained is less than 0.05. It can be seen, the significant value obtained is 0.000. This value is less than 0.05, so it can be stated that the regression model is fit. This significance value also means that the independent variables have a significant effect simultaneously or jointly on the dependent variable. Next for the coefficients table. This table is used for t test or hypothesis test. Here there are testing criteria from the T test on the coefficients table. If the significance value is less than 0.05, so it is concluded that there is a significant effect. And if the significance value is right at 0.05, so to find out whether or not there is an influence of the independent variable on the dependent variable, we can use a comparison of t count with t table. Then for the T test analysis, the significance value of the X1 variable, this X1 variable is 0.003. This value, the significance value of the variable X1. The significance value is less than 0.05, so it is concluded that variable X1 has a significant effect on variable Y. Then for the significance value of variable X2 of 0.023, this value is also less than 0.05, so it is concluded that variable X2 has a significant effect on variable Y. Then for variable X3 has a significance value of 0.238. This value is greater than 0.05, so it is concluded that variable X3 has no significant effect on variable Y. Furthermore, variable X4 has a significance value of 0.025. This value is less than 0.05, so it is concluded that variable X4 has a significant effect on variable Y. Then for the last variable, namely variable X5, where the significance value obtained is 0.273. This value is greater than 0.05, so it is concluded that variable X5 has no significant effect on variable Y. After we carry out the analysis on the T test or hypothesis test. Next we can perform multiple regression equation analysis. Where is the regression equation obtained, namely 4.737. This value is the constant value of the regression model, this number, added to 0.080, this value is the value of the regression coefficient on variable X1, this number, reduced by 0.201, this is the regression coefficient value of variable X2 minus 0.002, this is the value of the regression coefficient on variable X3, plus 0.298, for the value of the regression coefficient of the variable X4, plus 0.260, this value is the value of the regression coefficient of the variable X5. To recapitulate the results of the regression equation, like this. Then for the analysis, the constant value obtained is 4.737, so it can be interpreted, if the independent variable has a value of 0 or a constant value, then the dependent variable is worth 4.737. Then for the value of the regression coefficient on variable X1 a positive value of 0.080, so it can be interpreted that if variable X1 increases, so variable Y will also increase, and vice versa. Then for the value of the regression coefficient on the variable X2 is negative by minus 0.201, so it means that if the variable X2 increases, so the variable Y will decrease, and vice versa. Then for the value of the regression coefficient on variable X3 also has a negative value of minus 0.002, so it means that if variable X3 increases, so variable Y will decrease, and vice versa. Furthermore, the value of the regression coefficient on variable X4 has a positive value of 0.298, so it can be interpreted that if variable X4 increases, so variable Y will also increase, and vice versa. Finally, the regression coefficient for variable X5 also has a positive value of 0.260, so this means that if variable X5 increases, so variable Y will also increase, and vice versa. Well, I think that's enough explanation and practice how to do multiple linear regression analysis using the SPSS program. And I'm finish Wassalamualaikum warahmatullahi wabarakatuh