Understanding Regression Analysis: Independent and Dependent Variables (Full Transcript)

Speaker 1: Hi, I'm Zaree from Regression Analysis and I'm going to show you how to create a base space software. This is an independent variable. As you can see, this is a dependent variable. So, this is an independent variable. This is a dependent variable. It has a lot of effects. What are the effects of this variable? This is a regression. What is regression? Analyze. Regression Linear. This is a reset. This is a decam. Production efficiency. This is a dependent variable. This is an independent variable. This is a source. You can see all of them in the diagram. This is also a fueled rate. This is a confidence interval. This is a confidence interval. This is a firm rate of 10%. This is a peer rate of 5%. This is okay. And this is a regression analysis. Now look at this general status. Model summary. This is the co-efficient table. In the model summary, we can see the R-value and the R-square value. The R-value is 0.01. The R-square value is predicted to be 0.4%. We can see the product design, product innovation and assembly line. All of them have a significant relationship with the assembly line. The assembly line has a significant relationship with the product design and product innovation. The R-value is 0.07. The R-value is 1.003. We can review the reservation population as well. The product design is 0.05. The R-value is 0.05. The R-value is 0.05. 0.07% for product innovation 0.01% for product design 0.01% for product design 0.511% for product design 0.277% for product innovation 0.011% for product design