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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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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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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366 views

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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71 views

multiple imputation for k-means clustering + outcome variables

I’m exploring whether distinct clusters can be derived from real-time, smartphone logs of daily social behaviors, and how these clusters predict self-reported depression and loneliness. My plan is to ...
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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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Predicting elections: Probability distribution from betting odds and dependence

Suppose I want to find the percentage of votes each party will get in elections. I have following odds from betting companies. My first thought is to find the probability for each party achieving a ...
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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 ...
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Signal Detection Theory: correct rejections - what underlying processes?

Suppose we have a word recognition task, on the basis of which we compute the four rates defined in Signal Detection Theory as Hits, False Alarms, Correct Rejections, and Misses (HR, FAR, CRR, MR). ...
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Finding dependence of output variables on input variables

I want to perform a regression using neural networks. My input has 5 parameters [a, b, c, d, e] and the output is 4 variables [x, y, z, w]. Total number of observations I have is 1000. I wanted to ...
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What is the difference between having input parameters that are independent/dependent in ML tasks?

This is more of a general question. But, when you feed in inputs into a machine learning algorithm, are the inputs typically dependent or dependent? What are the implications if the inputs are ...
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Interesting and unusual dependent variable--how do you think it should be handled?

We have 2 dependent measures, which are both distributed from -4 to +4 in discrete steps. This is because the values are based on people's responses to four questions about whether they spend more ...
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Interesting variant on discrete probabilistic problem

Suppose $X \sim U(0,1)$ and $Y$ and $Z$ are random variables that depend on $X$. I've solved a problem where $Y$ and $Z$ are discrete (binary) and so finding the joint pmf just amounts to calculating ...
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61 views

SPSS Paired Samples T-Test outcome interpretation

Hypothesis: -Blood pressure will decrease after intervention Results (blood pressure): Systolic bp before mean: 102.00 Systolic bp after mean: 97.10 Diastolic bp before mean: 70.80 ...
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42 views

Sum of two dependent random variables with copula

I'm trying to calculate sum of 2 random variables by using Copula Theory in R or Matlab. However, I have very limited knowledge about probability. Actually I read a lot of theoretical information ...
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61 views

Within-person centering the outcome variable?

What do you think of within-person centering the outcome variable? If my research questions is clearly about within-person effects (in a longitudinal multilevel model), can I just use the centered ...
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65 views

Using predicted probabilities from logistic regression as dependent variable in a linear regression

I'm trying to run a Response Surface Analysis in SAS, but this is only possible with a continuous outcome, whereas my outcome variable is binary. I got the advice to first run a logistic regression ...
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Can a linear and logit model have the same shape?

While I was working on an exercise based this book, I discovered something interesting. When I fit a logit and simple linear probability model on the data (see code below), the predictions are almost ...
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Multiple comparisons, should I run a one-way multivariate analysis or can I use Mann Whitney?

I am analysing some study data and am unsure the appropriate statistical test to use. The study involves watching a film. 50% of participants saw version 1, 50% saw version 2 - (the difference in ...
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Can I do multiple ANOVAs without correcting for multiple comparisons?

In my study I have one repeated measures factor that I manipulated on two levels and one between-subjects factor that I manipulated on 3 levels (three groups), resulting in a 2x3 design. I also have ...
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71 views

Mixed Effects Models: Does Null Model Intercept Variance Set the Limit on Proportion of Dependent Variable Explainable by Full Mixed Effects Model? [closed]

I have a random intercept and random slope mixed linear model with a continuous dependent variable. To calculate ICC, proportion of dependent variable explained by model with predictors at Level-2 and ...
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Confounding variables in experimental study

We conducted a study to analyse the effect of tablet named 'xab' that help smokers to stop smoking. 5500 of smokers are selected. half of them were given different doses of tablet while the other ...
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355 views

Will Chi Square Test work for high number of categories?

I have a dataset of 1000+ records. In this dataset, I have two categorical features, Ticket-Label and Survived. Ticket-Label has 54 unique categories and Survived has 2 categories. The chi square test ...
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38 views

differences between conditional probability and dependency

Sometimes, I read articles about conditional probabilities and other articles about conditional dependency. My question what is the main differences between them? For example, "https://en.wikipedia....
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Analysing partially ordered responses

I'd like to analyse judges' decisions on bail. Ie some decisions (remand in prison) and clearly worse than others (unconditional bail). But in the middle there are a range of possible responses that ...
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Linear model for positive response variables

Very concise question: if I model a phenomenon which takes only positive values (for example, revenues or production) using the classical OLS, what are the consequences in terms of bias, efficiency ...
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115 views

How to use Correlation of variables to predict future pattern

I have data set of file generating from some user. User can generate 1, 2, 3,4, 5 and more files during a day. Data set example: Day one - 2 files generated - first is at 09:10 and second one at 10:...
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209 views

Diff-in-diff with mactched control group

I want to run a diff-in-diff model. To choose an appropriate control group, I use a nearest-neighbor matching model based on several determinants of the outcome variable that I study. I was ...
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327 views

Dealing with non-stationarity in panel data

I am using panel data for my analysis. My dependent variable is non-stationary while all my explanatory variables are stationary. Also, my dependent variable is a bounded variable (an index variable ...
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Random variable concept and terminology [duplicate]

I am a programmer with little mathematical background who started to study statistics/ML recently. I quickly stumbled upon the random variable term and it was hard for me to understand why in ...
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326 views

Dependent variables - BMI and Weight [closed]

I have to figure out, which of the two variables (BMI and Weight) is the dependent. So I can make a scatterplot. Can you please tell me which one? And why? I tried searching the internet, but without ...
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Does taking logs to supress hetroskedasticity only work for the dependent variable?

I have been told that by logging variables in a regression that hetroskedasticity of errors can be reduced. Is this the case also if only my dependent variable is logged?
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414 views

Odds ratio for continuous dependent variable by regression analysis

Can the odds ratio be calculated for a continuous dependent variable using logistic regression? If yes, kindly explain the procedure in SPSS. Further, can we apply logistic regression to an dependent ...