Linked Questions
24 questions linked to/from What is the effect of having correlated predictors in a multiple regression model?
2
votes
0
answers
943
views
Polynomial regression multicollinearity assumption? [duplicate]
The difference between Linear regression and Polynomial regression is that in the last we manipulate our original explanatory ...
1
vote
2
answers
438
views
Conflicting p-values of regressors in simple regression vs multiple regression? [duplicate]
Consider the results of the following code.
...
- 893
0
votes
0
answers
290
views
Why, intuitively, does redundancy in a multiple linear regression increase the standard error of the partial regression coefficients? [duplicate]
In his book "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences", J. Cohen says that redundancy in the independent variables of a multiple regression model increases the ...
- 707
1
vote
0
answers
72
views
Dissertation Results. X and Y non-correlation, but then X significantly predictive in regression model. Why? [duplicate]
I just finished my dissertation results and trying to interpret them and very confused. Basically, X and Y are not correlated but then when I put X in my regression model with a few other variables X ...
- 11
0
votes
0
answers
27
views
Why are the standard errors of these two outputs so different? [duplicate]
Context of the question:
Two professors want to look at the effect of students’ high
school math marks on their marks in their first calculus course. All marks are between 0
and 100. Using data from ...
- 1
73
votes
2
answers
27k
views
Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?
The coefficient of an explanatory variable in a multiple regression tells us the relationship of that explanatory variable with the dependent variable. All this, while 'controlling' for the other ...
- 1,575
50
votes
4
answers
61k
views
How to interpret coefficients from a polynomial model fit?
I'm trying to create a second order polynomial fit to some data I have. Let's say I plot this fit with ggplot():
...
- 697
22
votes
3
answers
56k
views
When can we speak of collinearity
In linear models we need to check if a relationship exists among the explanatory variables. If they correlate too much then there is collinearity (i.e., the variables partly explain each other). I am ...
- 805
9
votes
2
answers
2k
views
Does introduction of new variable always increase the p-val of existing ones?
I am doing some work that requires some estimates of gasoline oil demand elasticity on certain countries. After doing various econometric measures such as instrumental variable, I was able to get ...
- 235
3
votes
3
answers
4k
views
Visualising uncertainty in slope and offset for a regression line?
According to a least squares fit I have performed to my data, my slope is $-0.1038±0.033$, and my offset $0.1065±0.032$. My first idea was to visualise this by drawing three lines: $0.1065-0.1038x$, $...
- 1,439
81
votes
0
answers
64k
views
How can a regression be significant yet all predictors be non-significant? [duplicate]
My multiple regression analysis model has a statistically significant F value however all beta values are statistically non-significant.
All the regression assumptions are met. No multicollinearity ...
- 811
3
votes
3
answers
4k
views
Correlations between explanatory variables in regression
I'm just starting to learn about linear regression models and time series analysis and came upon the following doubt.
Suppose we have a variable $Y$ that we're trying to model using $p$ explanatory ...
- 211
3
votes
3
answers
2k
views
Modelling explanatory variables which depend on each other
I'm trying to estimate the value of an apartment, by doing a regression through similar apartments. The regression model looks now like this ...
- 471
1
vote
1
answer
3k
views
Effect of combining predictor variables in a regression model
Let's say I first run a linear regression model Sales = f(TV Spend, Digital Spend).
Now I add TV Spend and Digital Spend and run the second model. My second model is Sales = f(TV Spend+Digital Spend)...
- 41
5
votes
1
answer
433
views
Why one result is so wide in this logistic multiple regession
I am doing multiple logistic regression with data with 24 predictor variables and 193 rows.
All predictor variables have values of 0 or 1 and outcome variables (OUTVAR) also has only 2 possibilities. ...
- 10.2k