Linked Questions

0
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0answers
19 views

How to visualize multivariate linear regression? [duplicate]

When I have data which has one dependent and one independent variable, I can clearly explain it in cartesian coordinate system here is a regression line when my $y$ approximately equals to $2x + 3$ ...
5
votes
2answers
12k views

Predicted by residual plot in R

I'm wondering what the difference is between: 'predicted by residual plot' where I plot the residuals of the regression with the predicted values of the regression ; the case where I plot the ...
10
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1answer
2k views

Clarifications regarding reading a nomogram

Following is a nomogram created from mtcars dataset with rms package for the formula: mpg ~ wt + am + qsec The model itself seems good with R2 of 0.85 and P<0....
0
votes
1answer
7k views

R - Plot multiple regression line with confidence intervals with ggplot2 [closed]

Somewhat newbie question here. I'd like to use ggplot2 to plot my multiple regression model, but I ran into an error. I did some research on the forum before deciding to ask the community. I ...
2
votes
1answer
4k views

Plot regression with interaction in R

I did a regression analysis with the following variables: Predictor = dummy variable, dependent Variable = metric, moderator variable = metric. I now want to show my results in a figure. The ...
2
votes
1answer
3k views

Interpretation of odds ratios in a multiple logistic regression with interaction

I'm trying to formulate, for a report for non-scientists, the ouptut of a binomial model. However, I have some troubles with the log odds ratios, probabilities and stuff. I read some related topic but ...
2
votes
2answers
683 views

Put interested effect as IV or moderator in an interaction plot?

Say I am plotting the following interaction: mpg ~ cyl*disp (from the mtcars dataset in R). ...
2
votes
3answers
1k views

What is the meaning of the beta for the interaction between continuous variables in a linear mixed-model?

If I create a mixed-effects linear regression model similar to the following (using the lme4 package in R), where all of the fixed effect variables are continuous: ...
0
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2answers
1k views

visualization of interaction effects: using predicted values?

I am currently working on a study in which I am using beta regression (in Stata). My depend variable is bounded between 0 and 1. To interpret the effects of my independent variables I am using the ...
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0answers
2k views

Graphical Multiple Linear Regression in Stata

Consider this standard linear regression model: $Y = \beta_0+\beta_1X_1+\cdots+\beta_pX_p+\epsilon$ I've loaded such a dataset into Stata 12.0, so I have some variables $y,x_1,x_2,\dots,x_p$. How do ...
4
votes
2answers
78 views

In a regression problem, having a variable highly correlated with our target messes up the optimization of the parameters?

In a discussion with a colleague, she told me that if a variable X_i in our design matrix (X) is highly correlated with our variable of interest (target, y, etc), it will make the regression unsovable ...
2
votes
1answer
164 views

Visualizing a regression with 1 DV and multiple IV [closed]

I am working on a paper, attempting to explain the effects of certain influences on terrorist organizations with regards to politicizing. Using VISREG I am able to create visualizations to depict ...
1
vote
0answers
193 views

What is a fair way to visualize separate effects of multiple continuous predictors in a mixed-effects model?

I have a fitted mixed-effects model with a continuous dependent variable and multiple predictors that each vary by multiple random factors. In R, a simplified version of the model has the following ...
2
votes
1answer
68 views

Is the resulting function of plotting two variables imply that all other factors are being held constant?

Say, for instance, I plot two relationships (separately): (a) Share of income spent on food vs. household income; and (b) No. of houses in a unit vs. population of a city Then, for both (a) and (b),...
3
votes
0answers
32 views

Presenting result of bivariate regression to general public

We have a simple unvariate linear model of woodpecker abundance vs elevation: woodpeckers ~ elevation The model reports significantly positive slope for elevation. ...