Linked Questions
24 questions linked to/from What is the effect of having correlated predictors in a multiple regression model?
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Polynomial regression multicollinearity assumption? [duplicate]
The difference between Linear regression and Polynomial regression is that in the last we manipulate our original explanatory ...
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438
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Conflicting p-values of regressors in simple regression vs multiple regression? [duplicate]
Consider the results of the following code.
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290
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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 ...
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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 ...
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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 ...
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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 ...
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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():
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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 ...
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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 ...
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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: $0.1065-0.1038x$, $...
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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 ...
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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 ...
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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 ...
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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)...
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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. ...
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Correlation among the independent variables in logistic regression?
I am having a hard time to understand whether the independent variables in a logistic regression have to have some degrees of correlation. I came across a report that mentioned that in SEM the IVs are ...
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A fundamental question about multivariate regression
This is slightly embarrassing, as I've done a fair amount of statistical work, but for years I've heard this niggling voice at the back of my head, and I need to ask someone.
I remember when I first ...
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What can be said about about significant predictors in simple regression that become insignificant in multiple linear regression?
I have two predictor variables: An indicator variable A and a continuous variable B. My response variable is continuous (and also bounded, have not made it logit for reasons of simplicity).
In simple ...
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Do control variables in a regression analysis cause collinearity?
This is something that bothers me for quite some time, but I didn't find yet a satisfactory answer. I hope that the wisdom of the people hear will help me to clarify this:
In a multivariate ...
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Ridge or lasso regression to help out with significance issues in linear regression due to high collinear variables
In a linear regression I have two variables that are correlated with rho = 0.8. Given two multiple linear models where the two variables go in mutually exclusive, the estimare for each one is highly ...
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OLSR model: high negative correlation between 2 predictors but low vif - which one decides if there is multicollinearity?
Date, age, mrt and shops are all predictors in a dataset of 414 observations. Pearson's product-moment correlation shows a sizeable negative correlation between mrt and shops (-0.6 so definitely ...
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How exactly do the odds multiply when "evidence" adds up?
I was reading an answer explaining the justification for using the sigmoid function in logistic regression. The reason given was essentially "when evidence adds up, the odds multiply". This is the ...
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Multi-collinearity
I have a binary response variable (presence/absence) and four independent variables (min.temp, max.temp, precipitation and elevation. My scatter matrix is showing collinearity between 3 of the ...
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What do One-Sided Confidence Ellipses Look Like?
To make things concrete, take a simple linear model:
$$E[y \vert x_1, x_2] = \alpha + \beta_1 x_1 +\beta_2 x_2$$
In a one-sided hypothesis test, like $\beta_1 \ge k$ vs. $\beta_1 \lt k$, the ...
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