Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

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.

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. By convention, the dependent variable is put on the left hand side of the regression equation.

Historically, the phrase 'dependent variable' comes from the modeling of experimental data. The idea is that the dependent variable depends on the independent variables (is caused by them). However, 'dependent variable' is commonly used in other contexts, e.g., when working with observational data where inferring causality is potentially problematic.

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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, $e_i \sim N(0,\sigma^2)$, and independent, and $X$ is fixed, and …
colorlace
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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: $$\widehat{\log(y)} = \hat{\beta_0} +…
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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 variables are the usual real valued measurable…
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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 variable (DV). So, in contrary to a correlation it is…
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predict y variable that is only available as a sum by group

I would like to fit a model in order to predict a y variable, however in the training data the y variable is only available for groups of items (the x variables are available for each individual iteam). Here's an example of such a dataset created in…
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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% of total) I have started with 19 variables…
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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_{Y}(u) \end{equation} I have read somewhere "If…
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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 and consistency of the estimator? In other words,…
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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: carbohydrates, fiber, fat, protein and dependent variable…
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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 to depend on the values of other variables, which are…
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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 values of t each time I execute the command with the…
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Random forest with nonnegative dependent variable

I have a modeling framework with an outcome that must necessarily be positive. In the training data, the outcome ranges from close to zero to much higher (approximately 0.05 to 100). Is there a way to fit a random forest model for positive outcomes?…
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How to calculate this dependent probability (marbles without replacement)?

I present the question in two steps: First: Let there be 100 bags. A person puts 5 marbles into 5 separate, randomly selected, bags. You are now to collect the contents of the bags, one by one. If you have collected 3 marbles, you stop. What is then…
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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 dependent upon day of the week, month, and time of…
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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? > names(bb) [1] "BB1" "BB2" "BB3" "BB4" "BB5" > x1<-bb$BB1 > x2<-bb$BB2 > x3<-bb$BB3 > x4<-bb$BB4 >…
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