Regression Analysis ( Model Testing For Muticollinearity, Correlation Matrix, R Square, Etc.)

Multiple Regression Analysis, Multi-collinearity Model Testing, When Two or More Independent Variables Measure Same Thing, (Standard Errors are Large), Is Linear Regression Model Better When X's (Independent Variables) Are Combined Versus Used Separately ??, (1) Calculate Correlation Matrix (Multicollinearity), Need High Degree Correlation Between Y-Dependent & X's Independent Variables & (2) Need Low Degree Correlation Between X's Independent Variables, then Each X's Contribute To Regression Model, 𝐑^𝟐 always increases when Variables are added, but How does it affect 𝐑_𝐀𝐃𝐉 ??, Check If R Increases Or Decreases Upon Adding a Variable, detailed example by Allen Mursau

[@PierreYvesPau]

Love the graphics! Super cool.

[@lina478]

Thank you for the best explanation yet. You are an excellent teacher.

[@THEpushkar2001]

Great work,
in formula (1 minus) part is missed out.
R^2 adjusted = 1-(1-R^2)(n-1)/(n-k-1)
Many Thanks, for all wonderful videos.

[@manelnagarelmoken2003]

503 HERE in one video!!, that's great sir
but great work.... here.....

[@TheDHEL13]

Thank you!

[@mhany62]

great job

[@ajaykryadav]

very nice explanations..

[@Kindafu]

i'm getting 1.306 for the critical t value, when i input T.INV.2T(0.05,9)-1. Are you not actually subtracting the one in your operation? (That's the only way i am getting 2.306) Please let me know if i am doing this wrong.

[@jnscollier]

James Maynard Keenan from Puscifer knows stats too? Is there anything he can't do?

[@SmartLearn-Everything]

Can you send Me this docx or pdf or xls

[@MV-sy9bz]

so much here lol