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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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Can standardized variable be used to generalize sample results to whole population?

I have a dependent variable(wage), and two variables, that are correlated to wage (country and ...
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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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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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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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how to test for sufficient variations in the dependent variable [panel]

I am planning to perform a panel analysis with the sample size of 1020 individuals that have been assessed through their career ages. The panel is unbalanced (missing observations). My dependent ...
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Comparing the mean of predicted values for a misspecified model against the mean of the observed values

I have a regression that I have run on average ratings for some products (dependent variable) and their characteristics (Model 1). I have reason to believe there is a prejudice against a specific set ...
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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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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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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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48 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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synthetic datasets without any dependecies between features

I have to create a synthetic data set without any dependencies between features so that this equation should be hold. I thought about to take simply several Gaussians or randomisers, each would be ...
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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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3answers
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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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Covariance between nominal and continuous variables?

I have an nominal independent variable (two groups) and a continuous dependent variable. A two-tailed Mann-Whitney test yields a significant difference between the two groups (p < 0.03; my data is ...
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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 ...
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Where does the misconception that Y must be normally distributed come from?

Seemingly reputable sources claim that the dependent variable must be normally distributed: Model assumptions: $Y$ is normally distributed, errors are normally distributed, $e_i \sim N(0,\sigma^2)...
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Can I do regression without dependent variable?

I have a dataset of auto thefts that has the date, day, time the thefts occurred on. My independent variables would be day of the week, month, hour of the day, etc. I want to see if auto thefts is ...
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Is it possible to test an interaction between an IV and (dichotomized) DV and the DV?

My dependent variable (DV) showed a very high skewness (strongly positive), so I was wondering if I might integrate the mean, or median, split DV as additional independent variable in my model in ...
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Is there any value in correlating a binary dependent variable and categorical independent variables?

I'm constructing a logit model with a binary dependent variable (probability of exiting unemployment). On the right hand side (independent variables), I have a few continuous variables and a number of ...
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1answer
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My dependent variable is first difference stationary. Can I use the original variable?

I found my dependent variable is first difference stationary, but does this mean I have to use the first difference as my dependent variable? Or can I use my original variable as my dependent? My ...
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The law of large numbers mediates predictor-outcome relation, what to do?

I am conducting a random forest analysis in which a predictor is naturally correlated to the outcome. That is, the predictor is the amount of sunlight a patient received during his admission in a ...
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Dealing with probable outliers in the dependent variable

I am trying to fit a simple regression to a data set with ~45,000 observations. The dependent variable is revenue growth, but I'm concerned some observed values are incorrectly entered data. To ...
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Ratio variable as a dependent variable and a percentage as a independent

I am doing a unit-level linkage analysis using performance indicators (at unit level) from an audit to predict the percentage of a successful indicator. Basically, I am trying to see if the ratio ...
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1answer
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Reversed distribution of dependent variable in Stata's quantile regression

I am little confused by the fact that Stata seems to reverse the distribution of the dependent when calculating the quantile regression (any of the commands). When I look at the simple c.d.f. of my ...
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Emotion as dependent variable. Can I refer to emotion as a deep/broad dependent variable?

I am testing a hypothesis with emotion as my (Y) dependent variable. I am wondering if you can refer to emotion as a deep/broad dependent variable? Becausse it is may be influece by a broad range of ...
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How to rank dependent variables

Statistics and I are mortal enemies. I have an independent variable (gender) and four dependent variables (A,B,C,D). For each of the dependent variable, there were multiple questions with likert ...
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Dependent variable is continuous and categorical - how to regress?

My research is about password safety. As dependent variable I have the number of attempts a software needed to crack the respective passwords. This results in values in a range from approx. 1e+5 to 1e+...
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Multiple multivariate regression problem - auto correlated dependent variables

I have a classic multivariate regression problem, i.e. dependent variables are stored in matrix $Y$ having dimension $n \times p$. So $p$ observations come from the same respondent $i$ and we need to ...
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Is it possible to perform a regression analysis without a dependent variable?

I have a data set with 5 independent variables. Is it possible to do a regression analysis without the presence of a dependent variable? ...
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1answer
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Using continuous dependent variable which is already a probability (0,1) to predict probabilities (0,1)

I am building a model to predict probabilities based on the scores given by a logistic regression. I have tried cv.glmnet but it doesn't give the probability score, instead it gives scores lying in (-...
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2k views

Analysis of dependent variables

How does one use machine learning to identify/analyze the importance and impact of independent variables on dependent variables? Let's say I have a data set with independent variables being: ...
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Dividing a sample based on the value of y would be problematic?

Is it a well known fact in basic econometric that, if one divides a sample based on the value of Y (Dependent variable), it creates a number of problems? My dependent variable is financial rating ...
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Regression or classification with two dependend variables

I have a dataset with many variables about images and two dependent variables: one is a number that a person gives to that image and the other is which group that person belong (we have to groups, say ...
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1answer
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Testing for changes in variance following an intervention

I have a simple within subject design: I measure my variable (time point A), then do an intervention and measure the same variable afterwards once again (time point B) - my subjects are of course the ...
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Does p (dimensionality) include the dependent variable?

Let us say I have a dat set of the following: Weight Height Body Fat Index is the dimensionality of the data 2 or 3 or does it depend if I'm trying to do say K-means clustering vs say linear ...