Linked Questions

1
vote
1answer
2k views

When to not use R squared [duplicate]

I recently graduated graduate school and am looking for a proof on R squared. Specifically when to not use it. I really remember a professor impressing upon me multiple times not to report R squared ...
0
votes
1answer
943 views

The larger $R^2$ the better? [duplicate]

I want to show that the variable $X$ is significant. In model 1, I use financial statements variables, other macroeconomic variables and $X$. Here, $X$ is siginificant at a 10% level and $R^2$ is 0....
0
votes
1answer
1k views

What is a good R^2 value? [duplicate]

I understand the answer to this question is that it entirely depends on the data set. However, this does not help people understand if their model is suitable or whether they should explore other ...
0
votes
0answers
698 views

What do r (Pearson correlation coefficient) and R^2 stand for? [duplicate]

As far as I understood, R squared explains how much the variation in Y is explained by its linear association with X. And it's used as an indicator for goodness of fit of a linear model. Then when ...
0
votes
0answers
393 views

How to interpret the R2 in univariate regression analysis? [duplicate]

How to interpret the R2 in univariate regression analysis ?
5
votes
0answers
94 views

Is $R^2$ useless? [duplicate]

I stumbled on a discussion regarding the usefulness of $R^2$ as a metric. Where $R^2$ is defined as: $$ \frac{\sum (\hat{y} - \bar{\hat{y}})^2 } {\sum(y - \bar{y})^2 }.$$ The criticism is backed by ...
105
votes
20answers
30k views

What's a real-world example of “overfitting”?

I kind of understand what "overfitting" means, but I need help as to how to come up with a real-world example that applies to overfitting.
20
votes
6answers
55k views

Simple linear regression output interpretation

I have run a simple linear regression of the natural log of 2 variables to determine if they correlate. My output is this: ...
26
votes
4answers
49k views

Pseudo R squared formula for GLMs

I found a formula for pseudo $R^2$ in the book Extending the Linear Model with R, Julian J. Faraway (p. 59). $$1-\frac{\text{ResidualDeviance}}{\text{NullDeviance}}$$. Is this a common formula for ...
21
votes
3answers
3k views

Is a high $R^2$ ever useless?

In stats we're doing linear regressions, the very beginnings of them. In general, we know that the higher the $R^2$ the better, but is there ever a scenario where a high $R^2$ would be a useless model?...
14
votes
5answers
2k views

What does it mean for a linear regression to be statistically significant but has very low r squared?

I understand it to mean that the model is bad at predicting individual data points but has established a firm trend (e.g. y goes up when x goes up).
12
votes
6answers
35k views

Acceptable r-square value for multiple linear regression model

I'm currently working on my thesis, more specifically I'm analyzing some data collected from researchers about the project's they're working on. In the end, I have performed a multiple linear ...
16
votes
3answers
30k views

What is the relationship between R-squared and p-value in a regression?

tl;dr - for OLS regression, does a higher R-squared also imply a higher P-value? Specifically for a single explanatory variable (Y = a + bX + e) but would also be interested to know for n multiple ...
13
votes
2answers
9k views

Multiple linear regression for hypothesis testing

I am familiar with using multiple linear regressions to create models of various variables. However, I was curious if regression tests are ever used to do any sort of basic hypothesis testing. If so, ...
10
votes
2answers
18k views

How to choose the best transformation to achieve linearity?

I want to do multiple linear regression and then to predict new values with little extrapolation. I have my response variable in the range from -2 to +7, and three predictors (the ranges about +10 - +...

15 30 50 per page