Questions tagged [multiple-regression]
Regression that includes two or more non-constant independent variables. Also known as multivariable regression.
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How to interpret output of a moderation with 2 moderators (1 Dichotomous, 1 continuous) and a dichotomous predictor?
I have conducted a moderation analysis with two independent moderators. (Using Hayes Process model 2). I have attached the output for your reference.
I am looking for the impact that financial ...
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How to check confounding and mediation in large dataset?
Given a large dataset, one cannot possibly check every model. In particular, it does not seem clear to me that one can check confounding or mediation in either cases.
How does one check confounding/...
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Why is the value of SE for the transformed regression model higher than the initial model with autocorrelation?
Is it right that with positive autocorrelation, the model underestimates the SE. Hence, using generalized differencing, the transformed model has a higher value of SE? And if so, what happens to a ...
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Question about deciding whether something is a moderator or covariate
Based on what I know, a moderation analysis is simply looking at an interaction between an IV and a moderator. However, people often refer to moderators when they talk about ANCOVA (analysis of ...
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What does it mean if my confidence interval includes zero with a significant p value in linear regression analysis?
I performed linear regression analysis to assess the associations between continuous variables. I found a significant p-value but my confidence interval includes zero. What does it mean? Here are the ...
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how to measure the effect of a recurring event?
I have multiple cities with data on theatre visitors and an event as the mentioning of the theatre in the local news.
I want to estimate whether the event of a mentioning lead to more visitors for the ...
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Adjusting for multiple testing: unsure about number of tests to correct for
I am interested in comparing mean protein levels between 4 groups (Control, Stages 1-3), after controlling for age, gender and a genetic risk factor. My pre-specified comparisons are : Control vs ...
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Derivation of residual bivariate expression to find the value for a multiple linear regression coefficient
Could someone help me with this derivation? How do I get to this expression of $k$?
$y_i=\beta_0+kT_i+\beta_1X_{1i}+...+\beta_kX_{ki}+u_i$
$k = \frac{Cov(Y_i, \tilde{T_i})}{Var(\tilde{T_i})}$
where $\...
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Unbiased estimator of regression coefficient in high dimension
Is there any unbiased estimator for the regression coefficient $\beta \in \mathbb{R}^p$, p >> 1, where
$$
y_k = x_k^T\beta + \epsilon \in \mathbb{R}?
$$
Note that $x_k \in \mathbb{R}^p$ and $\...
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What's WRONG with my multiple regression model
I am working on a regression model, more precisely, multiple regression model for predicting one single value. I have a dataset of cars and some technical data.
For example, I have the following ...
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Most marketing channel have negative coefficient in econometrics model for sales?
This is the first time I am working on an econometrics model (Market Mix Modelling) for sales. I have weekly sales data and the number of impressions from various marketing channels like FB, Twitter, ...
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How to interpret fitted coefficients in a multiple regression model: binary, continuous, and interaction terms
Suppose I have a multiple regression model:
$y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2 + \epsilon$
where
$y$ is continuous
$x_1$ is dichotomous (0 or 1)
$x_2$ is continuous
If $x_1 = 0$, ...
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How to compare return percentages of products in different lifecycles?
For my project, I am trying to predict return ( when a product in ecommerce sale is returned) rate of products. For the same of simplicity, assume I have 3 static features (dont change in time) and ...
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What is the intercept in a regression model with demeaned dependent variable?
Suppose you have a regression model
$\tilde{y}$ = $X\beta$ + $\varepsilon$,
where
$\tilde{y}$ = $y$ - $\bar{y}$
and $X$ contains a constant.
If you estimate the model by OLS, does the estimated ...
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Multiple linear regression: Do all independent variables need to have good adjusted R-squared independently?
I'm very sorry if this should be obvious, I'm just feeling a little lost with this assignment..
I have four independent variables X1,X2,X3,X4 plus a constant, modelled against Y. I know X4 to be ...
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What is the correct search term to get information how to create tables like these? [closed]
I am quite a noobie in statistics. I am using R for some daily descriptive statistics but I cannot find good explanations how to make tables like these, or even suitable packages/libraries. GLM stands ...
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Covariance matrix of errors for homoskedasticity/heteroskedasticity
I've seen homoskedasticty and heteroskedasticity defined as the following
The error term of our regression model is homoskedastic if the variance of the conditional distribution of $u_{i}$ given $X_{...
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Transforming for assumption of normality (normality of residuals)
My model has a violation of normality assumption so the residuals are not normally distributed. I have tried log transformation and Box-Cox, but nothing worked. Any suggestion?
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Distribution-free prediction intervals in linear regression
I've found some literature on the subject, but it is rather difficult to read. I am wondering if the following simplified method makes sense. My question is what part is correct in this methodology, ...
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Should I use a heckman model?
I'm currently working on my master's thesis. For my research, I have to measure the impact of two independent variables (amount of infographics and amount of other images) on a dependent variable (...
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Linear regression has good performance in validation set despite not meeting the linearity assumption
I have a dataset with about 8000 samples and 18 predictors (16 continuous, 2 categorical). I am trying fit a linear regression, but despite trying multiple transformations, I can't make it meet the ...
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Identifying confounders in multiple linear regression
I am currently trying to identify confounders in a multiple linear regression, but I am a little unsure of a couple of steps. These are the steps I am taking:
Check to see if the potential ...
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Constructing a time dummy variable in DiD fixed effects model
I am using a Diff-in-Diff regression design to evaluate the impact of a county-level tax hike (i.e. treatment variable) on tobacco sales (i.e. outcome variable) in a given county, relative to counties ...
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Difference In Differences with Daily Numbers
I ran a DID regression and found my estimate on the DID coefficient to be .022. The units of time I am using are days, and at a certain day around halfway through my data, the treatment group was ...
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(Undergrad looking for help) In logarithmic regression (log-log), what does it mean if your explanatory variable is already a percentage?
So I'm hoping to a regression of Human Development Index against some economic variables I think could affect it. Some types of aid per capita, education spending by government as a percentage of gdp, ...
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Interpretation multiple linear regression with cumulative coding for ordinal variable
In order to do a multiple linear regression with categorical variable, I transformed them with the cumulative coding :
My problem is in the interpretation of the results of the regression : from what ...
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Confused about pooled statistics from multiple linear regression on multiply imputed data in R
I have a dataset with 75 cases. I did multiple imputation of missing data in SPSS. I am running a multiple linear regression on the data. I have 7 binary independent variables, one continuous ...
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Power with a hierarchical multiple regression analysis [duplicate]
I was wondering how to calculate the power with the below data? I did a hierarchical multiple regression with two models. There are a total of 87 participants and the alpha level is 0.05.
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How to calculate your power in GPower? [duplicate]
I was wondering how you calculated the power via Gpower with these results? There are a total of 87 participants and the alpha level is 0.05.
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Logistic Regression on multiple classes (Shouldn't it be only on binary?)
I'm a bit confused with the usage of logistic regression for multi-class classification. My understanding is that a logistic regression is dichotomous (two possible classes), so in the example of the ...
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build a linear regressor with labels in different scales
I just ran into this linear regression problem where the labels are in entirely different range for example for 25% of the samples, the labels are in [0.001,0.01], then for another 25 % of the samples,...
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linear regression with variables as linear combination [duplicate]
Consider a linear regression model with two variables x1 and x2. Suppose, I fit a new model with two new variables x1+x2 and x1-x2.
Are these two models equivalent?
What is the relationship between ...
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How to structure multiple linear regression model given dependent variables with multiple timepoints?
I'm working with a dataset that has multiple independent variables (e.g. height, sex, zip code, etc.) as well as dependent variables with multiple timepoints (i.e. test A results reported at 1wk, 2wk ...
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Help me find (ok, calculate/approx/uncover) the p-value in the following tables
For a meta-analysis I am looking for the p-values in these tables derived from reports dating back to 1980...
I am, by now, pretty sure I can't but maybe there is someone smarter than me here... If ...
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Relationship between regression coefficients in regression vs. reverse regression (with controls)
Question: How do the coefficients in a multiple linear regression (Regress y on x and controls Z) relate to the coefficients in the reverse regression (Regress x on y and controls Z)?
Simple Linear ...
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In a multioutput deep learning model, is there a benefit to normalizing the output dimensions if they are of different magnitudes?
I am building a multi output deep learning model where the output consists of five dimensions (the specific architecture is a modification of YOLO). These have different magnitudes (ranges: [0, 1.2], [...
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Dealing with multiple regression [closed]
details of my dataset
summary(lm(visits ~ health1 + age, data = Medicaid1986))
But it gives this output:
health1:The first principal component (divided by 1000) ...
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A faster way of finding unbiased estimators for this linear model
No access to computers or calculators is available for this problem.
Consider the following linear model
$$Y_1 =\theta_1 + \theta_2 + \theta_3 + \theta_4 + \theta_5 + \theta_6 + \epsilon_1\\
Y_2 =\...
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Time series regression with multiple independent variables
it's been a while since i studied statistics and im a bit rusty on the topic but im looking for some advice on times series regression.
I'm trying to generate a time series regression model where i ...
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interaction between (possible) correlated variables
Suppose I have a set of variables a,b,c,d,f,g,h,i, and I created a composite score by summing these eight variables into a composite score e=(a+b+c+d+f+g+h+i). Now I want to explore the interaction on ...
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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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Have my assumptions been met?
I am running assumptions for multiple regression and scatterplots are a real bane of mine. Can anyone advise as to whether the following scatterplot provides a linear or non linear relationship ...
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Inverse scaling of coefficient using SkLearn
I had constructed a simple Multiple linear regression model, where I have 2 independent variables and a target (dependent variable).
Now, I transformed my independent variable using ...
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Multivariate Regression - Proof regarding Constant
A colleague of mine thinks that the constant in a multivariate regression is equal to the mean of the independent variable, usually denoted by $\bar{y}$. I disagree with my colleague, yet I somehow ...
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Design matrix for getting desired contrasts in 3x3 design with 2 controls
We have nine conditions in our study (two category factors each with three levels; A: a1, a2, a3; S: s, c1, c2). I did two-way ANOVA; however, we are interested in a more nuanced question. c1 and c2 ...
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What should I do If I have 2 multi-categorical independent variables in a linear regression?
Ive been asked to perform a multiple linear regression of one dependant variable with several independent variables two of which are categorical. One has 3 options and the other has 5. I'm familiar ...
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In a 1-way ANOVA, is the square-root of the coefficient of determination the intra-class correlation?
The actual question I mean to ask is if the square-root of the effect size is the intra-class correlation (ICC).
When the 1-way ANOVA test is viewed as a bivariate analysis between a categorical ...
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Does my predictor in my multiple regression have too many variables?
So I am trying to work out what is the best predictor of a) awareness over environmental issues, b) concern over environmental issues and c) pro-environmental behaviour from a set of sociodemographics ...
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Deviance of a larger model is much larger than the deviance of the reduced(nested) model
I am doing a logistic regression with 8 variables but for some reason the model with the second degree of interaction has a much larger deviance than the nested model without interaction. Because of ...
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Is it possible that the correlation between $\hat{b}$ and $\hat{c}$ can be negative multiple linear regression? [duplicate]
Given the following linear regression model as following, with two explanatory variables $x_1$ and $x_2$ and response $y$
$$y_i=a+bx_{i1}+cx_{i2}+\epsilon_{i}$$
We say that $\hat{a}, \hat{b}, \hat{c}$ ...
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