Questions tagged [multiple-regression]
Regression that includes two or more non-constant independent variables.
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What type of regression for two groups of data?
I have data from 500 school children who took a test. 250 of the children have a certain type of disability (group A). Each child in group A was matched to a child on the basis of age, gender to a ...
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Can I have an IV which does not have main effect on DV directly but might have an interaction effect with another IV on DV?
I am inducing envy (IV 1) to see the effect on focusing illusion/anchoring bias (DV).
Since I am going to induce envy by showing attractive others' pictures,then gender will play a role because ...
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Multiple regression in SEM when the covariance between predictors is fixed to zero
I did a multiple regression with $x_1$ and $x_2$ predicting $y$, and found that I could recreate in SEM software (lavaan or AMOS) the same results I got doing things the regular way in R with the <...
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Using standardized residuals from a previous chi-square analysis as an IV in a regression
I have an analysis I'm considering, and I'm not familiar enough with the statistical details to tell if there are any potential errors. I've done my best to describe the analysis briefly below.
I ...
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2sls with interaction terms
I am running 2sls.
Dependent variable - y
Independent variable - x
Instrumental variables - z1, z2, z3, z4
Interaction terms - xz1, xz2, xz3, xz4
Control variables
Thus, the first stage of 2sls: x ...
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Poisson regression for rare events?
Poisson regression is commonly used to analyse count data. However, when we deal with rare events it does not seem to be appropriate any more. At least, graphical criteria to assess the model fit like ...
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Interpretation of Cohen's $f^2$ for effect size in multiple regression
I am struggling with the interpretation of the effect size of a multiple regression model measured by Cohen's $f^2$. I know the guideline to determine if the effect is small, moderate or high, but ...
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How to make a Ramanmetrix model accept values other than the Raman spectra for input? [closed]
I am working on a project to automatically evaluate Raman spectra from a production process.
It already works for some compounds with the chemometrics software Ramanmetrix (desktop version 0.5.0).
...
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Pros and cons of different methods for comparing betas in regression
In my line of work, we often hypothesize that one continuous predictor will have a stronger relationship with some outcome than another closely related (i.e., collinear) continuous predictor. We fit a ...
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Determining independent vs dependent variables for multiple regression models
I am trying to create a multiple regression model in Python that takes hours slept, minutes of exercise, and my average daily mood to fit a 3D surface of $1^{st}$ (plane) to $5^{th}$ order polynomials....
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Does anyone recognize this significance test between two regression betas?
I came across the following test for statistical significance between two betas (predictors) from a multiple regression model. Note that $R^2$ is the model coefficient of determination, $r$ is the ...
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Can I add a variable to a complex sample, and run a regression?
In a survey, a complex sample was collected, and the sample was designed to provide estimates at national level. In other words, individuals from one state were more likely to be sampled due to ...
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Time series model without ARMA component and with exogenous variables
I am trying to know what is the most simple model for time series data, with exogenous variables. What is the most simple framework I can use ? Is it possible to build a model more simple than ARIMAX, ...
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How to model this dataset?
I'm working with a dataset (my_dataset) that comprises six groups of individuals (Team_ID) with a dependent variable ...
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Regression to find optimal linear combination of ensemble of neural network weightings?
I have N neural networks trained on different subsets of features of a dataset and with slightly different methods. My problem is a multiclass output, with the output layer comprising of the softmax ...
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Mediation analysis with interaction effect
I am currently trying to amend a paper that I am writing in order to include the interaction effects (a4 and b4 in the spreadsheet) This results in the following sum-product $$a_1b_1 + a_2b_2 + a_3b_3 ...
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Interaction term switching sign of main effect
I'm estimating the simple model
communication_share ~ grid_size * n_procs. From the theoretical background I would expect a negative effect of grid size and a ...
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Trend variable in time series linear regression
tslm(Life_expectancy ~ Age + Gender + Race + trend, data=ts_df)
Can I do something like this? I actually tried using this as a model and I got a different outcome ...
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Variable selection in logistic regression [duplicate]
So I'm trying to make a multivariate logistic regression model in R studio. I'm not sure how to go about this. What seemed to make sense to me was to model every predictor against the response ...
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How to determine number of independent variables in regression?
I have heard that having more than one independent variable is useful when predicting human behaviour since it can be influenced by a combination of several factors!
Forgetting about the limitations ...
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Seasonal vs non-seasonal data regression
Is it correct to regress seasonal data with non-seasonal data in multiple regression? If not, how can I adjust the series to make forecasts?
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Dummy coding of linear regression, intercept and constraint
Let the following multilevel problem, where we try to predict the credit card balance of individuals $y_i$:
$$
x_{i 1}= \begin{cases}1 & \text { if } i \text { th person is from the South } \\ 0 &...
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How to interpret coefficients of nominal independent variables in Weka?
I'm struggling a bit with interpreting the output of a linear regression in Weka. This is my model:
...
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pymc3: Updating the standard error prior
I am estimating a Bayesian multiple regression using continuous data on both the dependent variable and the regressors. My goal is to iteratively estimate the coefficient distributions as more data ...
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Interactions in regression and choosing appropriate model?
I’m running the following regression model, where MR is a continuous predictor and grade, gender, and race are all categorical.
DV~MR + grade + gender + race
I’m also interested in how the 3 ...
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Modelling 3-level MLM of longitudinal data in R
I have two questions regarding modeling a 3-level multilevel model in R.
I have a dataset of different variables that were assessed 4x as part of a longitudinal study. At each of the four assessments ...
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Measuring total relative influence of groups in particular coefficients in a multiple linear regression
Suppose I have run a multiple regression model:
Y = B0 + x1B1 + x2B2 +..+ xnBn, weighted by w, from a dataset with such covariates and the weight variable of size N. Say there is another column in the ...
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Regression analysis with single outcome variable and binary independent variables
This is a meta-analysis. I'm looking at factors associated with vaccine acceptance in many countries. Looking at previous studies in different countries, I recorded factors that are associated with ...
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Predicting individual-level outcome with only group-level data
Suppose I have summary data from a number of different classrooms, and I want to model a binary outcome (pass/fail) for individual students. I have no individual-level data. I have some classroom ...
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Why is there a negative bias in this example of omitted variable bias?
I was learning the mechanics of omitted variable bias in the context of linear regression. I built the following simple model with R:
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Test validity of linear regression model - R
I have divided my dataset into train and test set, and I have run a linear regression on the train set.
However, how do I understand how well my model performs on the test set?
My code is:
...
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Which is the covariate, dependent and fixed factor in GLM
I am new to using SPSS and am getting confused when inputting variables. I am looking at how latitude impacts tree diversity, and how they differ between hemispheres, so want to look at the ...
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Why do we need to model lower-order effects in models with interactions?
I recently saw a paper with a four-way interaction. That already is difficult to interpret (maybe if you have 1 or more categorical variables but definitely near impossible to interpret if all ...
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Compare beta of single regression and multiple regression
Given that:
$$
\text{Corr}(Y, X_1) > 0 \\
\text{Corr}(Y, X_2) = 0 \\
\text{Corr}(X_1, X_2) > 0
$$
Consider 2 regressions:
$$
Y = a X_1 + \epsilon \\
Y = b_1 X_1 + b_2 X_2 + \epsilon
$$
Which one ...
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Moderation-analysis with a hierarchical multiple regression analysis
For my thesis I perform a moderation analysis via a hierarchical multiple regression analysis. More specifically, I want to investigate whether closeness in the parent-child relationship is a ...
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Robust way to add predictors to existing linear model
I'm looking for a robust way to gradually build up a regression model -- namely I have a linear base-model with a robust set of predictors for which I'm fairly certain I have near optimal weights for, ...
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When does SEM have little to no benefit over multiple regression, and there is a distinction without a difference between two approaches?
I recently saw a case where someone fit a SEM with 20 latent variables (with many indicators each) predicting a single latent variable (of several indicators), and suggested it was evidence for some ...
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Granularity of data sample for multiple regression
I have 10000 bricks. Each brick contains multiple physicians. I have features at brick level. I created a regression model with dependable variable as sales. Hence, I got sales at brick level in ...
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How to test if the effects of two covariates are statistically different?
I'm trying to model eating behavior with this model: $y=\beta_0+\beta_1x_1+\beta_2x_2+\beta_3x_3+\beta_4x_4+ϵ$
where y is BMI and the parameters are various characteristics of the food/drink a person ...
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Are these two definitions of the coefficient of determination $R^2$ equal?
I want to do multiple linear regression as explained on this Wikipedia site: I am given the following data:
$$
yx=(~(y_1,x_{11},\ldots,x_{1p}),\ldots, (y_n,x_{n1},\ldots,x_{np})~)
$$
of $n$-many ...
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Prove the relation between Mahalanobis distance and Leverage?
I have seen formulas on Wikipedia. that relate Mahalanobis distance and Leverage:
Mahalanobis distance is closely related to the leverage statistic, $h$, but has a different scale: $$D^2 = (N - 1)(...
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Choosing number of predictor variables for regression models based on sample size and desired power
Based on comments a previous question, I have changed my question.
I am wanting to see if the addition of "novel" variables to a model containing "traditional" variables improves ...
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Sample size and power calculations for multiple linear regression looking for an r squared increase [duplicate]
I want to perform a multiple regression, comparing a model with "traditional" risk factors, with a model that has two tested "novel" risk factors.
I'm want to make sure that the ...
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Knots in regression and the dummy variable trap
I am running a knot-like type of regression and have a couple of questions:
Imagine that we are working with daily data that spans over $3$ years.
Consider the following model:
$y_t = \beta_{0, t} + ...
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Does it make sense to talk of "multicollinearity" in the context of simple linear regression?
As far as I am concerned, "multicollinearity" referers to the presence of collinearity between two or more variables, even if there is no pair of variables that have a particularly high ...
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Can I do a multiple linear regression analysis with a mixture of raw data and index data?
I'm trying to do a multiple linear regression analysis in Excel using the Analysis Toolpak and I am not good at math, let alone stats. So please excuse my total ignorance. I'm using the following ...
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How to create a regression model object from intercept and coefficients values only (without the database) in R
I want to recreate a regression model based on what was given in a scientific paper. They gave intercept and coefficient terms.
I know how to create regression models in R, but is this possible to ...
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How to deal with interaction terms in regression that cannot have a negative product?
Assume we have the following model: $y = \beta_0 + \alpha_1 * x_1 ^{\beta_1} + \alpha_2 * x_2^{\beta_2} + \alpha_3 * x_1^{\beta_1} * x_2^{\beta_2}$ where as we have the following priors for our IV's $\...
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Standardizing OR from logistic regressions with log-transformed variables for meta-analysis?
I´m trying to meta-analyze odds ratios from logistic regressions; some of which log-transformed the independent variable first.
(i.e. some studies present an OR per +1 in the independent variable, ...
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Dealing with 0's in loglog regression by using indicator functions I(x > 0)?
Assume we want to estimate the following model
$y = e^{\beta_0} * x_1^{\beta_1} * x_2{\beta_3}$ which we can linearize into
$\log(y) = \beta_0 + \beta_1 * \log x_1 + \beta_2 * \log x_2$
Assume that ...
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