Questions tagged [assumptions]

Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some way. Theoretical results about estimators usually require assumptions about the data generating mechanism.

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The Same or Different [closed]

Is the assumption autocorrelation for logistic regression the same as linearity of independent variables and long odds assumption?
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Automatic methods to forecast many monthly sales time series

I'm barely a student in his internship, so I don't have a lot of experience, but I am facing an issue with a project I'm having at my work. I'm a bit in the dark because I find it hard to understand ...
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What exactly is violated if your response variable exhibits autocorrelation in ordinary least squares regression?

I'm trying to understand the issues with using OLS regression when our data exhibits autocorrelation. Let's say you simulate a process where: ...
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Rationale for fitted vs residuals plot

Why does examining fitted vs residuals plot help us determine whether there is heteroskedasticity or not? Could someone give me a detailed theoretical rationale for this test? Does the randomness of ...
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LDA vs. QDA when assumptions do not hold

If the Bayes decision boundary is linear and the underlying distributional assumptions are Normal, we expect LDA to perform better than QDA on the test set. But if the Bayes decision boundary is ...
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Is the linearity of Y and X an assumption for linear regression? [duplicate]

There are many posts regarding linear regression, so I'm sorry I'm still coming back to this subject. However, I still have some questions about it. I know for sure that the model should be linear ...
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When using a gaussian link in GLM, what are the assumptions?

In R, when I am fitting a model glm(y~x, family = gaussian(link="log")), do I assume that $Y \stackrel{iid}\sim N(\mu, \sigma^2)$ or do I assume that $Y \stackrel{...
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DiD with Interaction - Assumptions

In my research, I would like to answer the question whether a regulation change leads to my continous, independent variable (X) affecting my outcome variable to a greater extent for those being ...
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Machine Learning: 1-way ANOVA from data collected where all samples are not independent?

I am analyzing machine learning imagery classification by using 3 different algorithms to classify imagery and a sample of ground-truth points to collect an overall classification accuracy. I wish to ...
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Realistically, does the i.i.d. assumption hold for the vast majority of supervised learning tasks?

The i.i.d. assumption states: We are given a data set, $\{(x_i,y_i)\}_{i = 1, \ldots, n}$, each data $(x_i,y_i)$ is generated in an independent and identically distributed fashion. To me, ...
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ANOVA assumptions without raw data?

I have several mean values (with standard deviations and sample sizes) that I would like to compare. However, I do not have the raw data. Is there a way to test the assumptions of ANOVA in this case? ...
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Vector Autoregressive Models (VAR) and error terms correlation

Before all I want to clarify, I am not looking for a direct answer in the following question but more of a clarification. I am doing a master's degree and the question is part of an assignment, so i ...
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Assumption of error of logistic regression [duplicate]

I know there are debates about whether the error exists and its distribution in the case of logistic regression. Suppose we assume that the error term follows the logistic distribution. Are we ...
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normal distribution is essential in t test to compare two independent samples?

In student t test to compare the means of two samples, whether the normal distribution of each sample is prehypothesis or not? As we know, t test is used for comparing two independent small samples ...
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A bit confused on p values on ttest in Stata output

In Stata the output of a ttest shows p values as: Pr(T<t), Pr(|T|>|t|) and Pr(T>t) I understand the two sides to be the tails of the test and the middle ...
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Conditions when the log of Relative Risk is approximately Normal

Under what conditions is the log of RR (or OR) approximately Normally distributed? Sources would be very much appreciated - wikipedia for example just states the SE formula but provides no information ...
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meaning of error term being correlated with regressor

I have encountered the statement that "the error term and one of the regressors are correlated" a few times and I am having trouble understanding what is meant exactly. Let's say we have a DGP $$y=\...
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Deviation from Normality Assumptions - t-test and f-test

Note that I am not a statistics major, but someone who is applying a workflow informed by statistics to a technical problem encountered at work. I've done some statistics at University, however the ...
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Why do regression estimates provide lower relative error than averaged values?

I am trying to estimate the per-cell protein concentration for some samples. I have performed a series of protein extractions for each of my samples, with each extraction using an increased (and known)...
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What R package to use to test assumptions for Structural Equation modelling?

I am looking for a way to test assumptions for Structural Equation Modelling. My model has observed variables that together predict one latent variable, which in turn predicts another observed ...
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Maximum sample size for one-way ANOVA?

Lists of requirements for one-way ANOVA include the following: Samples should be mutually independent Samples should be from a population with a normal distribution Samples should have the same ...
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1answer
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Conditional variance [closed]

I'm struggling to understand why the following is true: MLR5 assumption in multiple regression $\text{var}(u|x_1,\ldots,x_n)=\sigma^2$ implies that $\text{var}(u|x_i)=\sigma^2$ for every $i$, ...
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How to prove an OLS estimator is inconsistent

I have two equations $Y_i = \beta_0 + \beta_1X_i + \epsilon_i$ $X_i = Y_i + Z_i$ and additional information that $cov(\epsilon_i, Z_i) = 0$ And I need to prove that using the OLS in the first ...
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Huge sample sizes, tests, and deviation of assumptions?

I am performing Wilcoxon test (but in theory it can be any) and sometimes my sample sizes are huge. Even a single outlier may cause an extremely low p value. I am not interested in small effects at ...
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Is there a more elegant way to understand violating OLS weak exogeneity besides computing the variance directly?

Let $X\in \mathbb{R}^{n\times p}$ be a design matrix, $\hat{\beta}=[X^TX]^{-1}X^Ty$, and $\epsilon \sim N(0,\sigma^2 I_n)$ Under weak exogeneity, OLS typically assumes $$E[\epsilon_i] = 0$$ $$E[x_i^...
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What are the preconditions to fit a vector autoregression model? [duplicate]

can we fit a VAR model if we have a mixture of I(0) and I(1) variables?
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Bayesian model assumptions [duplicate]

I have never worked on Bayesian modelling, especially in R programming and i am struggling with finding the assumptions that need to be checked after the Bayesian model fitting. Are they the same with ...
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Linearity assumption of linear regression

According to this website, if the scatter plot follows a linear pattern (i.e. not a curvilinear pattern) then linearity assumption is met. Here is an example where the assumption is not met. But ...
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What are the assumptions in bayesian statistics?

So, for OLS there are 3 assumptions regarding the DGP, which are (from Stock & Watson): Independence of error terms (+ Homoskedasticity?) IID of variables Large outliers are unlikely, meaning non-...
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Considerations of surrogate end point measures

Data from various studies indicate that men who exercise are less likely to sustain a bone fracture compared to men who don’t exercise. For a number of reasons, it is difficult to use bone fracture ...
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Would you accept this normality for ANOVA? (asked yesterday, but data revised and different graph)

I posted yesterday asking for help with ANOVA assumptions, however I discovered the data I was given had several incorrect values. This data fulfills the assumption of homogeneity of variance, and the ...
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One-Way ANOVA assumption: No Outliers in boxplot

I read in this link: enter link description here It said: ...
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Help interpreting residual vs fitted plot and normality (ANOVA on R)

I'm carrying out a statistical analysis on R using ANOVA and am not sure if the data meets the assumptions of normality of residuals or homogeneity of variance. My data : And my plots: Any help is ...
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Mixed ANOVA - can I ignore Box's M if sample size is equal in each group?

I have read that Box's M tests can be ignored for MANOVA given equal cell size, is the same true for mixed ANOVA? For example, if I have an equal number of participants in two groups who each complete ...
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Assumptions of L1 Regression [duplicate]

I know that the L2 regression (regression-based L2 loss function/least square regression) assumptions are as follows. 1- Little or no Multicollinearity between the features. 2- Homoscedasticity ...
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Relationship between strict exogeneity and exogeneity in Time Series ADL models

I am currently learning about ADL models in Time series regression. The textbook notes down two types of exogeneity: Strict exogeneity and exogeneity. Exogeneity is defined as $$E(u_t|X_t,X_{t-1},...)...
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Rule of Sample Proporitions - What is the minimum sample size for a 2 Proportion Z Test?

I've been reading different resources, and I've been confused over the requirements for the minimum sample sizes to perform a 2 Proportion Z-Test. I'll be listing the rule, and the corresponding ...
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Are the assumptions of linear regression fulfilled? (Dummy variables only)

the dependent variable of my linear regression are stock returns of different companies. I have determined these stock returns with an event study and would now like to test the influence of different ...
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Do we need parametric tests? [duplicate]

First of all, sorry for catchy title, my question is not that broad as it suggests. I just came to conclusion that I don't need parametric tests. Instead, I need some feedback if my reasoning makes ...
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When should you check if assumptions are met when using stepwise selection?

Suppose I want to find a linear model with Gaussian error for a given data set. (The data set contains insurance claims and the end goal is to predict claim cost from claim features.) Also, suppose ...
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Assumptions for difference-in-difference regression using panel data

I'm doing a difference-in-difference analysis to measure the impact of removing financial incentives from healthcare quality indicators. I have data from 450 clinics, for 26 indicators in the '...
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poisson vs ordinal regression

I have an outcome variable in my dataset that follows a Poisson distribution Y Frequency 0 52121 1 2831 2 34 0 - None, 1 - Moderate, 2- Severe : ...
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What are assumptions of generalized linear model?

can anyone help me and tell me what I have to check to conduct the generalized linear model in SPSS. (What are assumptions of generalized linear model?). thank you so much for any try to help.
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Centering and scaling each subject individually - mixed effect models

I have a repeated measures design with two factors (A,B). For each subject, variable C is measured 7 to 10 times in each combination of A and B. My first approach was to scale and center each ...
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conditional independence mixed linear models

I'm analyzing an experiment using linear mixed models but am not sure whether my model is appropriate or whether I'm violating the assumption of conditional independence. I've asked a statistician at ...
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Assumptions failed in experimental design model

I'm dealing with a three-factor experimental design with two-factor interactions. The problem is model residuals does not fit any assumption (normality, homocedasticity and non-correlated residuals) ...
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Number of Variables in Elastic Net

I have a data set with 1000 observations and 150 independent variables. When I apply elastic net, I end up with 100 variables. I wonder if I need to do any additional feature selection or if I can use ...
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Are the errors in this formulation of the simple linear regression model random variables?

On page 21 of Applied Linear Regression, fourth edition, by Sanford Weisberg, the error $e_i$ for case $i$ under the simple linear regression model is defined to be $y_i - E(Y | X = x_i)$, where $E(Y |...
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ncvTest vs Graph reliability

I am performing Multiple linear resgression The ncvTest has a p value of 0.02 or so which means that 95% level we have heteroskedasticity but the graph looks well and randomly distributed quite good ...
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Homogeneity of variances but non normal residuals of a two way ANOVA

I have a problem, actually two. I am doing a bunch two-way ANOVAs and some of them can't fullfil the assumptions, no matter what I transform the data to. Question: Does there exist a suitable non-...

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