**3.207 multiple-regression questions.**

Software packages that calculate regressions sometimes also return p-values. I want to understand how to calculate this p-value by hand.
Here's what I think I understand:
I want to calculate the ...

Is there a table of range for beta coefficients used in multiple linear regression that we can use to interpret if influence is strong, very strong, weak or very weak. Thanks!

I have a multivariate linear model (y=x1+x2) which gives me the following results when using R's plot() function:
I can clearly see that the Normality and ...

I have a query related AIC value. I am getting very high AIC values while selecting multiple regression model, ranging from 4300-4600. Is it possible to get such high AIC values?

I am running a study on school children to compare psychometrics in physically active and sedentary children.
My IV is physical activity with 2 levels - physically active / sedentary
and DV ...

I have a 1-10 Likert scale as my DV and five variables as my IV. My IVs include both continuous and categorical variables.
I am wondering which independent variables have a major effect on my ...

I'm working with a data set of housing prices in Seattle from Kaggle.com. I have identified 7 variables that are significant in a linear regression model for modeling/predicting the prices of the ...

I have performed (backwards elimination) stepwise regression using some fMRI data predictors to model spectroscopy data as a DV. This has resulted in some interesting models.
I now have some ...

I am having trouble with interpreting the estimates. I took the exponent of the estimate. If it is negative I get a result that makes sense. For example, .55 means X has a 55% less chance to exiting; ...

I have to run a linear regression analysis with an interaction effect of two categorical variables:
Modality (audio, visual and audio-visual)
Repetition (1x, 2x and 4x)
I have already dummified the ...

Byars et al.'s paper "Natural selection in a contemporary human population" includes a multiple linear regression of number of children (LRS, lifetime reproductive success) on several variables (...

I would like to build a regression model to predict an outcome variable, y. Let ymin, ymax be the smallest and largest observed values of y in the dataset. Let ymean be the mean observed value. The ...

I read that these are the conditions for using the multiple regression model:
the residuals of the model are nearly normal,
the variability of the residuals is nearly constant
the residuals are ...

As a neuropsychology graduate student with some experience in statistics (I'm usually the guy other psychologists come to with statistics problems after trying it themselves but before seeing a ...

Does the Stagewise algorith stop when the gradient reaches 0?
What if the algorithm never reaches the gradient=0, is there any valid criteria to be used to stop it?

I want to use forward selection to choose predictors in a multiple linear regression model. If you have a regression with N predictors and want to add another predictor, is there a way to update the ...

I have here an R example with highly correlated predictors. How can I show that OLS doesn't predict well on highly correlated predictors?
...

Is there any example of RandomForest with multiple output that can help me understand better how to compute it, whether it is using MultivariateRandomForest library or other ?

I am just trying to explain to people with very little knowledge of statistic about MLR.
I basically Have this question:
Why when we have the number of independent variable ($X$) and dependent ...

I have been assigned to help create a new sales model. My natural instinct is to try to find trends among current customers within the data that I have been given so that we can more effectively ...

I'm trying to run a multiple regression on a dataset in R.
The structure of the data that I want to use for the regression is as followed (only showing the variables I want to use):
...

Best-subset regression analysis:
I want to test effects of differents ecological variables on my response variable. I am working with function glmulti() of glmulti ...

Suppose I have a multi regression model, dummy variables and interactions included. How am I suppose to filter through them all in order to choose the best model?
I KNOW this question was asked on ...

We want to investigate which variables determine the final grade in a University exam (say Y_2), which can assume integer values between 18 and 32.
We think that Y_2 depends on:
1) Personal variables ...

Must the moderator variable correlate significantly with the dependent variable, if I am to do a moderation analysis (multiple regression)?
I have a predictor (X) and an outcome (Y). They correlate ...

The lm function in R can print out the estimated covariance of regression coefficients. What does this information give us? Can we now interpret the model better or diagnose issues that might be ...

I'm trying to do predict using multiple linear regression in R. I have been able to do the multiple regression bit, by converting raw data to data table. However, when i'm trying to use predict ...

Is it wrong to standardise a variable (e.g. polygenic risk score) and then centre it for use in multiple regression?

What should be dependent and independent variables and Why should they be used?

Background
I am trying to compute by "hand" (using excel) the confidence interval width at each point for a multivariate glm with interactions.
Questions:
For the interaction terms, do I make a new ...

The formula for the multiple coefficient of correlation of two independent variables ($x_1$ and $x_2$) and an dependent variables ($y$) is this:
What is the formula for three ($x_1$, $x_2$, $x_3$) or ...

I just reviewed very good example of fitting StackingRegressor from mlxtend package.
...

I have extremely large number of observations (8524152) of soil moisture, precipitation, evapotranspiration, delta precipitation, and delta evapotranspiration. I ran a multiple linear regression model ...

How can we interpret the results in the graph below?
Does the R command lm() uses Heteroskedasticity-consistent covariance matrices(HCCM) estimators, or HAC estimators for the inference tests?
The ...

I am considering a linear regression model $Y_i = X_i^T \beta + \epsilon_i, i = 1,2,\dots,n$. where $X_i \in \mathbb{R}^p$. $\epsilon_i$'s are independent copies of random error $\epsilon \in \mathbb{...

I have got results from a multiple regression model, and I'm confused about how to interpret them.
I'm confused by the high standard error, could that be caused by my outliers?
Second, I've got high ...

My results of a multiple regression model show high standard errors, meanwhile the spss casewise diagnostics showed a large portion of outliers (which were not actually outliers). Is it due to those ...

I am currently working on my bachelor thesis in finance and I faced some problems regarding my dataset. I wanted to analyze the effect of leverage on the performance of companies and as many ...

I believe the rule of thumb is at least 10-20 observations per predictor variable, but I was hoping to get some additional clarification.
Suppose a hypothetical example with dependent variable of ...

Stepwise regression had been overused in many biomedical papers in the past but this appears to be improving with better education of its many issues. Many older reviewers however do still ask for it. ...

I would like to show the independent variables (IV) contribution in percentage. For example,
Regression equation is ...

I have a set of observed values ($y_{1Obs}$) and 3 predictive variables (n = 27). I use multiple linear regression to create a linear model:
$Z_1=\alpha_0 + \alpha_1 W_1 + \alpha_2 X_1 + \alpha_3 Y_1$...

If we have one independent variable, then the given regression coefficient $\beta$ = Pearson's $r$. If we have multiple independent variables, how can calculate Pearson's $r$ for each variable if only ...

I have the summary results of a linear regression model with 26 covariates which has been fitted to two independent samples (with different sizes). Now I am planning to use the linear regression beta ...

Right now, I'm trying to use some georeferenced data to predict revenue for some stores.
My data set has 21 obs (Annual Revenue for 21 stores), but I have 306 variables (Population/pop density, area, ...

Is the following methodology correct:
Fit a multiple linear regression model
Obtain the standardized coefficients
Sum up the absolute value of all standardized coefficients
Divide each individual ...

I am running a Logistic regression on 6 independent variables and running chisquare tests shows high degree of association between 4 independent variables. Most of the topics suggest running first uni-...

I hope someone may has some tips for me which Model I should set up for my Research question.
Think of the following Design:
cognitive tests were conducted at ages 5, 10, 15, 25 (cT)
every person ...

I'm investigating the influence of several independent variables (IVs) (measured on the party,district level and individual level) on individual level campaign behaviour of ordinary candidates (index ...

- regression
- r
- linear-model
- regression-coefficients
- categorical-data
- multicollinearity
- interaction
- logistic
- correlation
- time-series
- least-squares
- multivariate-analysis
- machine-learning
- generalized-linear-model
- statistical-significance
- hypothesis-testing
- residuals
- self-study
- anova
- interpretation
- r-squared
- predictive-models
- data-transformation
- model-selection
- econometrics

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