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
0
answers
235
views
High $R^2$ on Ordinary least squares model with violated assumptions. Is it good?
Recently I tried to fit some points which (from the plot) seems linearly distributed.
The fit result (in R) is:
Residuals:
Min 1Q Median 3Q Max
-112223 -2532 2021 3698 …
18
votes
4
answers
3k
views
Why is Ordinary Least Squares performing better than Poisson regression?
I'm trying to fit a regression to explain the number of homicides in each district of a city. … a Poisson regression. The problem is that I have better results in the OLS regression: the pseudo-$R^2$ is higher (0.71 vs 0.57) and the RMSE as well (3.8 vs 8.88. …
2
votes
0
answers
272
views
Exploratory data analysis. What features are important?
I am trying to fit the number of crimes in a city with some enviromental variables (aka my features). I'm using a Poisson/Negative Binomial model since I have count data.
The problems are:
selecting …