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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Replicating a study with a discrete dependent variable in linear mixed effects model (1-4 scores) [duplicate]

As the title says, I want to replicate a study that runs linear mixed effects models with a dependent variable that is discrete, with scores from 1 to 4. So, I have two main questions about that. 1 ) ...
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Dependent variable standardization in neural networks

I am using a multilayer perceptron model to predict urban temperatures. I have standardized the independent variables before training the model. However, I have not standardized the dependent variable....
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Dependent variable has no variance error in logit regression

I m running a logit regression with over 90,000 observations. However the case when dependent variable =1 , is only 115 observations as per the data, the rest are 0. The Eviews software shows "...
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Compare Paired Data from 2 Timeframes of Different Lengths

I need to compare dependent samples (matched-pairs) from the same group in 2 time frames, simply a hypothesis test to determine if there is any statistically significant difference pre and post an ...
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Standard deviation of a computed response

I have the following, mean values of 2 model parameters corresponding standard deviations of those 2 parameters correlation coefficient a computed response a measured response standard deviation of ...
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categorial (binary) dependent variable with several predictors: which study type?

I plan to conduct a logistic regression with a categorial dependent variable and several predictors. Anyway, I want to pregister my study but I could not find out how I would name this study type. ...
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Growth rate: how to avoid division by zero

I'm calculating the growth rate in productivity of some workers after an event. I have the output of the workers after the event, let's call it b, and the output ...
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Is percentile a good method?

Hello I'm an undergraduate student doing research about prevalence of carpal tunnel syndrome among college students I want to follow the method of this research (prevalence of carpal tunnel syndrome ...
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Using knowledge about response distribution to improve element-wise predictions

To illustrate what I mean, assume the following simple true model: $$ Y_i = \beta_0 + \beta_1X_i $$ Note that there is no error term, $\beta_0$ = 1, $\beta_1$ = 2, and $X_i \in [0,1]$. This implies ...
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Is this the correct test to perform?

In my research I have 4 independent variables which are gender, BMI, department, and hours spent on computer and two dependent variables which are test scores for two different tests (SSS) and (FSS). ...
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How to do a MANOVA if my three dependent variables have only two degrees of freedom?

In my experiment-design I have 3 dependent variables. They are related to percentages of time during which the subjects exhibit a certain behavior. As they always must exhibit exactly one of these ...
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How to find the probability of multiple successes?

I am struggling to find a probability formula for the grouping of outcomes. For example if I were to have 16 balls 1 is green and 15 are red. If I have to group them in sets of 2, so 8 sets of 2, the ...
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When can one combine events?

I do not know whether the question is too broad or not. Suppose I have a group of population. I want to study association between certain risk factors against some(at least 4) outcomes. I will assume ...
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Transforming only y variable with Yeo-Johnson in Python

I have 4500 values in my dependent variable and 75% of values are zeros (no negative ones) The distribution looks like this. In multiple sources I read that Yeo-Johnson transformation can be a ...
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Correlation coefficient as a dependent variable in regression analysis [closed]

Since my knowledge of statistics is less than perfect I decided to ask if my research methodology is acceptable. Is it possible to use a correlation coefficient as a dependent variable in a regression ...
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Using a feature as a target denominator

Could you please tell me what (bad) can happen if I use the same feature as the denominator in the target feature and as the predictor in a boosting regression? I think I should exclude it from the ...
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Probability bound of the difference of order statistics for correlated and identical Gaussian random variables

Suppose, there are $n$ identical and correlated Gaussian random variables namely, $X_1, X_2, ..., X_n$ with $X_i\sim\mathcal{N}(0,\sigma^2)$ for all $i\in\{1,2, ...n\}$. The correlation coefficient ...
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Are there predictive benefits to Box-Cox/Yeo-Johnson on an outcome/dependent variable?

Suppose we have a simple regression model with non-normal residuals. Transform the outcome variable with Box-Cox or Yeo-Johnson and fit a linear regression. Evaluating on a suitable test set, take the ...
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Perform glm on presence/absence data

I have a large presence/absence matrix of genes from different strains of S. aureus. I conducted a PCA and observed differences between my conditions. Now, I would like to find out which genes drive ...
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Statistical Test for pairwise data

I have 3 disjoint groups $A,B \text{ and }C$. Each group contains $N_g,g\in\{A,B,C\}$ models and each model is associated with data (training and testing) which is denoted by $(M_i^g,D_i^g),i\in\{1,\...
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Is it appropriate to use difference scores in this context?

I am trying to compare the level of rating accuracy of two groups of participants (say Sample A and B). The study design is as is: I asked a consumer panel to rate how much they liked three products. ...
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Central limit theorem for dependent binary-related variable

Let $Y\sim N(\mu, \sigma^2)$ and given sample size $n$, we have an iid sample $\{Y_1, ..., Y_n\}$. We sample $X$ (size $n$) from Bernoulli with probability $\pi$. Denote $Z_i=X_iY_i$. Then, when $X_i=...
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What are the best algorithms to predict a continuous target where I only have binary attributes? [closed]

Hello, I am new to machine learning and have a project where the dataset consists of binary attributes and the target("Pawpularity") has a continuous value. I was wondering if you could ...
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Best test to evaluate change over time in discrete non parametrical variables

I have to analyze a database that includes a basal score from a functional status (score 0-10 in discrete intervals of 0.5) and a subsequent evaluation of the same subjects with a variable interval of ...
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Outcome variable coding for multinomial logistic regression with "I don't know" choice option

I have a categorical outcome variable "Type of intervention" with 3 levels: "Type A", "Type B", & "cannot decide". The "cannot decide" option is ...
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Is the physical impact / effect necessarily the independent variable / dependent variable of the regression model

A regression analysis (RA) is often explained as follows: "...Regressions analyses are statistical methods, by which you can calculate, whether an independent variable (IV) impacts a dependent ...
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Alternative to 2 dimensional χ² test which assumes one dependent and one independent variable

A 2 dimensional χ² test can 'prove' that nominal variable A is associated with nominal variable B, and can express the strength of that association using φ[0,1]. But what test can I use to test the ...
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In a chi-square test of association, what are the "dependent" variables?

The chi-square test of association is used to determine if there is an association between two categorical variables. In statistics, we call "dependent variable" a variable that is supposed ...
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How to treat proportions with different sample sizes as response variable in GLM / GLMM?

For an ecological research project, I am trying to model the effect of different factors on the prevalence of a specific pathogen in ticks. Ticks were collected from around 80 different plots and ...
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Quantile regression showing different results with same tau

I am using the quantreg package from R to calculate quantile regression between 2 columns : red pixel values and near infrared pixel values (target). But the problem is that it gives me different ...
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Should correlated independant variables be removed to the expense of the adjusted r_squared and rmse?

I have a dataset with a target variable and multiple independent variables. Some independent variables are highly correlated with each other (sometimes r>0.9). First, I thought i'd create a linear ...
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Can the number of Target features (y) exceed the number of Input features (X)?

I am trying to perform a train_test_split() on a dataset. Before doing so and while I am assigning data to X and y variables, I realized I have 8 Input features a.k.a. Independent variables and 45 ...
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what are the dependent and independent variables [duplicate]

I am measuring Covid anxiety using likert scale, according to people age, gender, pcr result, and educational level. Which variable can I consider to be dependent and which independent?
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target definition for churn model

I'm not sure if this question is duplicated, but I was wondering how to set the target for a churn model, or a "payment-delay detection" model, which I think is similar to a churn model. The ...
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How to express a likelihood function conditioned on a filtration?

Suppose we have the sequence of discrete dependent variables $\{Y_t\}_{t=1}^n$ and we are interested to express its joint likelihood function conditioned on an entire history of a process $X$, where $...
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Creation of a Target Variable - with Domain Expertise

Is there anything intrinsically wrong with defining your own target variable based on one's domain knowledge? For example, let's say I have client data - demographic, psychiatric history, etc. If I ...
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What should i do with this kind of data? (price from the ads and actual price)

There are two subsamples in the dataset - on one the target is real(valid), and on the other it is approximate (I don't know how it differs yet, on one sample the real price of an apartment, and on ...
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Does Cramer's condition imply strong mixing?

In Theorem 1.4 of D. Bosq the Cramer's condition is a prerequisite for the tail bound of sum of dependent variables. The Theorem is as follows: Let $(X_t,t\in\mathbb{Z})$ be a zero-mean real-valued ...
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How to measure the strength of association between two variables where majority of pair are assosiated?

I am quite new to the stat so facing a huge problem in result extraction, I have a large dataset running ~19000 (genes) x1500 (patients). I would like to see the dependence between two variables (one ...
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growth curve modeling for nominal and ordinal dependent variables

Is it possible to conduct growth curve modeling for nominal and ordinal dependent variables instead of a continuous variable? The aim is to investigate the nature of the growth trajectories and to ...
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Which test for an ordinal dependent data in a pretest posttest design

This is a pretest-posttest design with a control group (no treatment between measurements) and an experimental group (treatment between measurements). All measurements consist of the same rubric and ...
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Predicting limits of bounded dependent variable in Random Forest

I am new to machine learning and trying to use Random Forest to predict a bounded dependent variables (percentage from 0 - 100). The majority of the training data points (~80%) are at the limits of ...
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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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Live fibrosis score as dependent variable in multivariable logistic regression that includes elements of the score as independent variables

There is an index for liver fibrosis called the FIB-4 score which is calculated from blood concentrations of the liver function enzymes AST and ALT, platelet count and age. The score is included in ...
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GLM in R: How to set the dependent variable as a number of events (rows in dataset)?

I am carrying out GLMMs with glmmTMB, and my dataset contains rows of video analyses (behaviour analyses of recorded videos of deer) and columns of different data (camera id, date, time, behavioural ...
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Statistical modelling - choose the horizon of prediction?

In a Statistical Modelling / Machine Learning framework, I have a tabular data set $X$. For each instance I have a history of a binary outcome of what happen after the instance is measured. ...
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Query regarding Models in MANCOVA?

This is the query in the area of Mancova in SPSS. There are 8 dichotomous IVs and 7 DVs. IVs are shape components of the bottle like deep curve, slim, parallel etc. and DVs are like sophisticated, ...
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Measuring dynamic interactions between time series?

I have some "difficult" time series, say, X, Y, Z, and W such that W is influenced by X, Y and Z which are independent. All of these time series are non-stationary and non-linear. Suppose we ...
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Can I use linear regression analysis in order to remove estimated effect of an independent variable so that I can find the effect of another variable?

I have a dependent variable (DV) that is influenced by an independent variable (IV). However, I know that it is also influnced by another factor (F), for which I don't have any values. My hypothesis ...
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Why on a Confusion Matrix are Independent and Dependent Axis Transposed?

Normally, a graph's X axis contains the independent values and the Y axis contains the dependent values. However, on a Confusion Matrix, the X is dependent (predicted) and Y is independent (true). Why ...
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