Insights Blog
-- Browse All Articles --
Physics Articles
Physics Tutorials
Physics Guides
Physics FAQ
Math Articles
Math Tutorials
Math Guides
Math FAQ
Education Articles
Education Guides
Bio/Chem Articles
Technology Guides
Computer Science Tutorials
Forums
General Math
Calculus
Differential Equations
Topology and Analysis
Linear and Abstract Algebra
Differential Geometry
Set Theory, Logic, Probability, Statistics
MATLAB, Maple, Mathematica, LaTeX
Trending
Featured Threads
Log in
Register
What's new
Search
Search
Search titles only
By:
General Math
Calculus
Differential Equations
Topology and Analysis
Linear and Abstract Algebra
Differential Geometry
Set Theory, Logic, Probability, Statistics
MATLAB, Maple, Mathematica, LaTeX
Menu
Log in
Register
Navigation
More options
Contact us
Close Menu
JavaScript is disabled. For a better experience, please enable JavaScript in your browser before proceeding.
You are using an out of date browser. It may not display this or other websites correctly.
You should upgrade or use an
alternative browser
.
Forums
Mathematics
Set Theory, Logic, Probability, Statistics
Linear regression and random variables
Reply to thread
Message
[QUOTE="fog37, post: 6852793, member: 503639"] Hello Dale, The sample data, i.e. all the available pairs ##(x,y)##, are modelled as following: ##Y## is a random variable and its expectation value of Y is ##E[Y|X] = \beta_1 x+ \beta_0##. The regression model that we compute generates estimates of ##\beta_1## and ##\beta_0## which are ## \hat{\beta_1}## and ## \hat{\beta_0}##. The regression model itself is ##\hat{\beta_1} x+\hat{\beta_0}##. Does that mean that the regression model estimates the mean of ##Y## and not ##Y## itself? We use the regression model ##y_{pred}= \hat{\beta_1} x+\hat{\beta_0}## for predictions of the ##y## values though... [/QUOTE]
Insert quotes…
Post reply
Forums
Mathematics
Set Theory, Logic, Probability, Statistics
Linear regression and random variables
Back
Top