The F test indicates whether the model predicts significantly better than predicting only with the Constant (think: just using the mean to predict). The r square tells you how much of the variance in the model is explained by the predictor. You want the F test to be significant because it means that your predictor(s) tells you something more than just the mean, but the r-squared tells you how much more information you are getting from the predictor over just using the mean. F = better than nothing; r = how much better. Hope that helps.
@@ResearchByDesign Thank you very much! You are such a good teacher! I have followed most of your videos. Btw, I would like to see videos about multi linear regression ^ ^
Yes, the criterion variable could also be called a response variable (or sometimes a dependent variable), but either way, those would refer to the variable on the Y axis. Pretty much everyone calls the X variable the "predictor".
Do both F and r square indicate the significance of the model? I get confused when F and r square were explained in the video.
The F test indicates whether the model predicts significantly better than predicting only with the Constant (think: just using the mean to predict). The r square tells you how much of the variance in the model is explained by the predictor. You want the F test to be significant because it means that your predictor(s) tells you something more than just the mean, but the r-squared tells you how much more information you are getting from the predictor over just using the mean. F = better than nothing; r = how much better. Hope that helps.
@@ResearchByDesign Thank you very much! You are such a good teacher! I have followed most of your videos. Btw, I would like to see videos about multi linear regression ^ ^
@@ResearchByDesign how do you know the f test is significant or not?
Hello Dr. Daniel, I'm commenting all the videos I guess haha! I have a quick question is X, criterion variable also called as response variable?
Yes, the criterion variable could also be called a response variable (or sometimes a dependent variable), but either way, those would refer to the variable on the Y axis. Pretty much everyone calls the X variable the "predictor".
@@ResearchByDesign Thanks!