Hi, I really like your explanation of how to use G*Power for Linear Regression. I was able to follow it verbally; however, it was very hard to see on my screen. I am not too tech-savvy to suggest how to fix this. However, if you could find a way to fix this issue, your video would be used more often by us G*Power newbies. 😉

Hey^^, first I wanna thank you for you great videos. They' re helping me a lot. I'm analyzing the data for my bachelor Thesis right now and in this video you stated that if levenes Test shows Variances are significantly different its not a problem anymore that the boxs-m-Test says there is no variance-kovariance-homogeneity in my data. Do you have an article i can cite for this in my thesis? Thank you very much in advance for your answer^^

You are simply a super human. Love your explanation

hello my teacher, thank you very much for the video :) Can ı ask? if our categorical variables were 2,3,6,2 then DF=(2-1)*(3-1)*(6-1)*(2-1)=10 would it be calculated like this? So DF=10 would be?

I have a question I am doing two seperate multiple regression analysis do I have to test the power for both multiple regression analyses ?

My understanding of the Chi-square in EFA is that this statistic measures the discrepancy between the observed and expected covariance matrices. A lower value indicates a smaller discrepancy, suggesting a better fit of the model to the data. Thus, the p-value associated with the Chi-square statistic indicates the probability that the observed data could have occurred under the null hypothesis. A high p-value (typically greater than 0.05) suggests that the model fits the data well, while a low p-value (typically less than 0.05) suggests a poor fit. Though keeping in mind that the Chi-square test is sensitive to sample size. With large samples, even small discrepancies can lead to significant Chi-square values, potentially suggesting a poor fit even when the model is reasonable.

You are a lifesaver❤☺️

Could you explain further about the F value and degrees of freedom and why/how these are significant if P value is < 0.05? And also what do the Durbin-Watson numbers signify?

Thank you for the tutorial. I understand why you rejected the null with the P value from the anova table. But could you explain what the P values indicate in the coefficient table?

Thank you

I think you need to use a correct effect size calculated.