Questions tagged [dependent-variable]

In a regression model, the dependent variable is modeled as a function of other variables (regressors). Other common names are 'response', 'outcome', 'predicted variable', 'criterion', 'target', etc.

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How to interpret the coefficient of an independent binary variable if the dependent variable is in square roots?

I am trying to analyse the impact of a trade policy on exports. My dependent variable for the main specification is log of exports but as a robustness check, I want to include a linear transformation ...
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Logistic regression model predicts only one outcome, producing a high specificity but very low sensitivity. How do I improve the model?

I'm designing a logistic regression model to predict hospital mortality. Why? To identify 'adjusted' odds ratios for a variable of interest on mortality. Methods: - set up using a training dataset (75%...
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Are these the major response variable types?

I am a biologist with an interest in experiment design. I recently refreshed my memory about what kinds of response variables that are possible, and made this chart (see jpeg). I would like feedback ...
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Adjusting the response variable to incorporate scaling effects [duplicate]

I am using a random forest regression to model a count of species from a number of different survey areas. Each survey area has a different size. My question is how to model the response variable, to ...
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Using baseline categorical response as a predictor

I have a longitudinal data where the categorical response is collected at two-time points. I was wondering if it's possible to adjust my categorical response at baseline as a predictor and run a ...
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Which statistical theory to use in modelling disease with rainfall data?

I am trying to see the rainfall effect on a disease. I have almost 100 years of rainfall data and after just taking a simple average I am getting the monthly average rainfall data. I also have average ...
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Isolating causal effect

we are going to have kids working together. So for example, there could be 20 groups of 3 students each. What we would be interested in seeing is if the presence of particular students affects the ...
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How to compare effect of one Independent variable (time) on different dependent variables

For my research I have measured 3 different biomarkers with different scales (e.i. two are concentration and one is a ratio) inside the same population at time 0 and 2h and 4h after food ingestion. I ...
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missing value in the binary outcome when performing the propensity score matching (PSM)

I'm doing the propensity score matching by using R studio. Sometimes I have missing value in the binary outcome, e.g., I wanted to see 'development of significant coronary artery disease (CAD) on ...
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Is it possible to use a discrete variable coded as a continuous one as a response variable in a PGLS?

Also, do you know any equivalent to phylogenetic logistic regressions for binary dependent variables (Ives and Garland 2010) for multistate dependent variables? Reference: Ives and Garland. 2010. ...
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Asking for feedback on the application of a Central Limit Theorem

Let $\{X_{n,i}:1\leq i \leq d_n\}$ be a triangular array of mean zero random variables where $d_n$ is a positive increasing sequence ($d_n\leq n$). Under some conditions, a Central Limit Theorem ...
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Individual effect of different variables where one explaining variable depends on the other in a non linear way

I have data on native and invasive species along a height gradient. I want to know what effect the number of native species has on the number of invasive species (or the portion of invasive species). ...
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effect_plot, ggpredict, ggeffect: how to appropriately plot predicted values for a multiple linear regression?

I have a linear model with one main predictor of interest (GABA) and three covariates of no interest (BMI, Sex, Leg.Length): ...
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perquisites of the Chi-square test of independence

I want to use the Chi-square test of independence to test the following two variables: Student knowledge v.s. course attendance The null hypothesis is: student knowledge and course attendance (X and ...
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Covariance between two data sets using trained Gaussian processes

I have two data set A and B, both are $n\times d$ matrices. I trained two Gaussian processes at each data set, GP_A and GP_B. Now, I need to find the Cov(A,B). I want to now when can use Cov(GP_A,GP_B)...
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Can you move an independent variable to the dependent side of the model?

Consider a biochemical reaction f(x1, x2, x3,…), where x1 is substrate and x2, x3, etc… are relevant process variables such as temperature, pH, etc…, and f(x1,x2,x3,…) is the product of the reaction. ...
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Multivariate regression with a mix of ordinal and continuous dependent variables [closed]

I would like to run a multivariate mixed regression MCMC model with two response (independent) variables, namely x and y. x is continuous while y is ordinal. There is one predictor variable that is ...
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Diversity enhancing sampling from positively dependent random measures

Is there any efficient method to maximize diversity of samples when sampling from a set of positively dependent random variables?
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Whether to cap the dependent variable while treating the outliers?

So I am trying to run a linear regression model in R where the objective is to identify what's driving the credit card spends including both primary and secondary. I have a dataset with 10000 obs I ...
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Lagged (in)dependent variables: 2 time periods

Summary I have a dataset with observations regarding an industrial process in two time periods. My goal is to find predictors of future performance, and I am wondering whether panel data regressions ...
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Sample stratification by dependent variable in linear regression analysis

I have a theoretical question that I would like some guidance on. Is it ok to stratify a sample population by the dependent variable? Does this bias regression results? For example, I'm doing an ...
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questions about different ways to define a rate (such as fertility rate) as a dependent variable in a regression

Lets say I want to run a regression of fertility on some independent variables at the county level. There are a couple options of how to do this, one would be to just create the fertility rate by ...
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Comparing two approaches to modeling dependence in bi-variate Gaussian regression

Presume we would like to model the dynamics of two related variables as a bi-variate Normal, while also accounting for the effect of other covariates (via regression). E.g. I would like to model ...
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Can I build a statistical model for a dependent variable based on other dependent variables?

I have a question about statistical models. In particular, whether it is correct/meaningful to build a statistical prediction for a response variable based on the other dependent variables from the ...
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Which method of analysis to use on SPSS for two dependent variables (likert scale) with repeated measures?

I am conducting research for my master's thesis regarding the phenomenon of Hindsight Bias. I have four experimental conditions in which the DVs include foreseeability and inevitability, and they are ...
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Composite outcomes

I am quite new and not an expert in statistics. I am just wondering what is the best way to assess differences between treatment arms in a trial in the primary composite outcome including all events ...
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Dependent variable count or continuous?

If my dependent variable is " How many hours do you drive per day"... and respondents have answers in positive integer numbers like(10 hours, 12 hours), will it be considered count data or continuous ...
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Using a logistic regression on censored data

I am interested in modeling the probability of default (PD) of a loan product. Data I have a dataset going back several years. Most of the loans have reached their terminal state (paid off or ...
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If the dependent and independent variables are not stationary, but they are first difference stationary. Can I use the original variable?

If the dependent and independent variables are not stationary, but they are the first difference stationary. Can I use the original variable?. If I use the percentage change for the dependent and ...
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Conditionally Dependent Variables

I'm curious, since I do not know the name to this phenomenon, how do we handle variables that are conditionally dependent in the following sense. E.g in case of bank. Relationship: A checking ...
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Are dependent variables necessarily functions of one another?

The problem Suppose you have two variables $X_1,X_2$ so that $X_1\not\perp\!\!\!\!\! \perp X_2$. Do we necessarily have that a functional relationship exists between them? I am assuming random ...
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How to Handle Conditionally Dependent Variable

Correct me if I am wrong, but from what I've been reading for Machine Learning models, it is the procedure to find independent input features that are correlated to your target variable. However, ...
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A question about characteristic functions

A probability 101 question. We know that if two variables $X$ and $Y$ are independent then the characteristic function $\phi_{X+Y}(u)$ can be written as \begin{equation} \phi_{X+Y}(u)=\phi_{X}(u)\phi_{...
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Predicting y from log y as the dependent variable

In the book Introductory Econometrics by Wooldridge the chapter, which deals with predicting values of $\hat{y}$ (chapter 6.4 in the 5th edition) states the following: If the estimated model is: ...
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multivariate analysis with mixed numerical and categorical dependent variables

I have one independent variable (yes/No) and several covariates (sex/age and so on..), five dependent variables (four continuous, normal distributed and one categorical as yes/no). I want to look up ...
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Array size as a random variable in graphical models

Assume that I want to model a mixture of sentences. There are two different sources generating sentences with specific sentence length and word distribution. I had came up with the following graphical ...
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Coding of response variable avoiding dichotomization

Frank Harrell and many others say - dichotomization should be avoided for the power maximization, it can easily be checked using simulations: Wilcoxon test is more efficient than any other form of ...
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Why does adding a dependent variable to a MANOVA remove significance?

We designed an experiment with 2 discrete independent variables (IV), 1 (measured) confounding variable, and 2 dependent variables (DV). We hypothesize that the two IVs interact on both DVs. Right ...
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How related are the categories of a categorical dependent variable

There is a set of numerical and categorical independent variables and one dependent variable that is categorical. Some of the categories are far to be related with the rest, but most of them are very ...
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Is there a maximum amount of dependent variables for MANOVA?

I have a dataset where the influence of 3 manipulations (vs placebo) is tested on 17 different outcome measurements. To avoid having to run 3*17 = 51 ANOVAs to see which type of manipulation has an ...
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Predict with average values of dependent features

I created linear regression model to predict story points by individual team member (in sprint). Since story points are relative by sprint team, I trained my model after scaling story points to 0 - 1 ...
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Which statistical test should I use for testing dependent variables across 5 datasets?

5 pupils are given the task of hand drawing a hundred trapezoids with a given perimeter. The vertices are marked A,B,C,D counter-clockwise starting with the longer parallel edge (A and B). The lengths ...
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Cross-classified multilevel model with lagged dependent variable Using R

I am a bit stuck with my model and I wonder if this is even possible using R. Basically I want to use a lagged dependent variable (LDV) in a cross-classified multilevel model (MLM). Following remarks ...
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Test for the influence of a manipulation on multiple dependent measurements (order effects, multiple comparisons)

I have a dataset which looks like this: ...
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Quantitatively assess if independent variables are sufficient to determine dependent variable behaviour

I have the matrix of the independent variables X with dimensions n_samples x m_features and a vector ...
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Measuring mutual dependencies between variables. The most fundamental relation

One has a simple dataset of 3 independent variables, e.g., x, y, z. Now: y and z are logically connected (this is known a priori) and indeed a nice & tight correlation (small scatter) between ...
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Independent and dependent variables Machine Learning

I want to start a regression model on lottery numbers, my database consist of the dates, and results(numbers). In order to do a regression model I believe you need independent and dependent variables, ...
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Have $(U,V)$ be a pair of Bivariate Gaussian variables with mean $0$, variance $1$ and $Cov(U,V) = p$ where 0 < ρ < 1 [duplicate]

Have $(U,V)$ be a pair of Bivariate Gaussian variables with mean $0$, variance $1$ and $Cov(U,V) = ρ$ where $0 < ρ < 1$ I'd like help finding the density of $U+V$ So far I have tried to use $...
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What do these SAS plots imply about the regression outcomes?

I have run a Tobit regression, and in the output the following charts are generated (at the end of parameter estimates). I cannot relate these charts with the parameter estimates. Could someone give ...
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Decomposing a random variable into marginals and copula

I’m having trouble getting understanding how to actual construct a copula, from my understanding it captures the purely joint features of a joint distribution. I’ve been working with the following ...