Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
-2
votes
For a simple linear regression, do we have requirements for using continuous/discrete variab...
For regression following variable types hold:
Dependent variable must be continuous only.
Independent variable may be continuous or discrete. … If dependent variable is discrete then the problem will become classification problem rather than regression.
As per me
An Introduction to Statistical Learning
is good book to learn regression. …
0
votes
What is meant by multicollinearity does not influence the predictive power of the model?
Multicollinearity makes the (observation)×(feature) matrix singular or near-singular.
This is why it reduces the predictive power of the model.
For more clear explanation follow the given link:
ht …
0
votes
Linear Regression feature transformation
Answer to your first question:
Linear equation means linear combination of the features/variables.
In linear equation we focus on the fact that the combination of features must be linear. Here we doe …