Thank you brother for a video lecturing. I've one question of clarity if sign. (p>_0.05) how could I interpret the final report? I'm a bit confused in that case!
When the p-value associated with a coefficient in a multiple linear regression is greater than or equal to 0.05, it suggests that the corresponding predictor variable is not statistically significant in predicting the response variable. In other words, there is a higher likelihood that any observed relationship between this predictor and the response could have occurred due to random chance. Therefore, you might consider removing this predictor from the model if your goal is to create a more parsimonious and interpretable model. However, it's important to also consider the practical significance of the predictor and its role in the overall context of your analysis before deciding to exclude it.
Thank you very much! This is really what many young researchers looking for. Keep going, Dr; outstanding work.
thank you for your attractive and clear lecture
Thank you so much. Really appreciate your video. Clear and well explained.
It is Short and clear. Thankyou!!!
I must say you did something important! Thank you!
Thank you very much I was confused and your presentation helped me to understand well. Keep up the good work.
Thank you so much for sharing this helpful knowledge.
Thank you, Dr God bless you! Very much appreciated!!!! 🙏🙏
Thank you really brief explanation & lecturing Thank you........
Thank you! You solve my problem of multiple regression on spss
Thank you so much. Really appreciate your presentation & knowledge sharing
Thank you very much ,stay blessed qenu mulu alegeba bilogna neber 💚💛💙
Excellent. Thank You. Next
thank dr.
Thank you brother for a video lecturing. I've one question of clarity if sign. (p>_0.05) how could I interpret the final report? I'm a bit confused in that case!
When the p-value associated with a coefficient in a multiple linear regression is greater than or equal to 0.05, it suggests that the corresponding predictor variable is not statistically significant in predicting the response variable. In other words, there is a higher likelihood that any observed relationship between this predictor and the response could have occurred due to random chance.
Therefore, you might consider removing this predictor from the model if your goal is to create a more parsimonious and interpretable model. However, it's important to also consider the practical significance of the predictor and its role in the overall context of your analysis before deciding to exclude it.
You saved my thesis
It's good and i am learning alot. Could you do on SEM, Multilevel analysis???
Great! please make more videos...many can learn
Thank you very much .
Thank You very mcuh
Thank You.
thank you🙏🙏🙏
Thank you
Thank you a lot brother
thank you Dr. my question is that how to calculate sample size for poisson regression. please help me