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 ...

learn more… | top users | synonyms

0
votes
0answers
6 views

Is it meaningful to look at predicted values vs residual plot to assess homogeneity of variance assumption for mixed ANOVA?

I have two-way mixed ANOVA. I remembered getting the a single value predicted values when I plotted predict vs residual plot for one-way repeated measures ANOVA. So is it meaningful to produce the ...
3
votes
0answers
20 views

How to deal with failing the proportional odds assumption in ordinal logistic regression

I am attempting to do ordinal logistic regression but I keep failing to pass the proportional odds assumption. Almost all of my features are shown to have high significance, but the only model that I ...
4
votes
3answers
119 views

Goodness of fit test on sparse contigency tables with high dimensionality

I have a vector of size 1x3500, which can be viewed as the 'known distribution'. It is simply a table of counts across 3500 groups (i.e. a contingency table). I also have $N$ other vectors of the same ...
0
votes
0answers
45 views

Zero Inflated Versus Negative Binomial Models Conundrum

I have a count variable that represents the number of new band foundings in a country-year. However, there is zero inflation as there are no foundings for most country-year. There is also ...
0
votes
0answers
26 views

which variables should I include in a multiple regression analysis?

From what I understand, one should only include an independent variable in a multiple regression analysis if it meets the assumption of linearity. I have six variables, three of which are correlated ...
2
votes
1answer
62 views

Omitted variable bias and the constant term

For omitted variable bias to occur when a variable is left out of a regression, there is one axiom and one condition that must be fulfilled: (Axiom) By definition, the coefficient of the variable ...
0
votes
2answers
77 views

What to do first when there are violations of assumption in Simple Regression? [duplicate]

Suppose we want to do simple linear regression. Before we do simple linear regression, we need to check these following assumptions (please correct me if I'm wrong): Linear relationship Normality of ...
1
vote
1answer
75 views

The distinction between stochastic independent variable and measurement error in independent OLS variable

Assume that OLS regression of the form: $$Y_t = X_t'\beta + u_t$$ Suppose $X_t$ are stochastic, thus standard Gauss-Markov assumptions need to be accommodated. Given that: $$\text{E} {(\hat\beta)} ...
0
votes
0answers
8 views

Unequal levels of independent variable in regression with non-randomized groups

I'm running a multivariate regression (multiple continuous DVs) that also has multiple predictors (1 two-level categorical, 1 continuous). The categorical predictor is the group participants were in, ...
0
votes
0answers
34 views

For what classes of models is the assumption that the residuals sum to zero relaxed?

My question is related to an interesting suggestion about relaxing OLS assumptions by @alexis back in May 2014: Assumptions of linear models and what to do if the residuals are not normally ...
8
votes
3answers
633 views

Do we really need to include “all relevant predictors?”

A basic assumption of using regression models for inference is that "all relevant predictors" have been included in the prediction equation. The rationale is that failure to include an important ...
10
votes
3answers
565 views

Does the assumption of Normal errors imply that Y is also Normal?

Unless I'm mistaken, in a linear model, the distribution of the response is assumed to have a systematic component and a random component. The error term captures the random component. Therefore, if ...
1
vote
1answer
41 views

Why can we assume normally distributed errors in probit but not in LPM?

Why are we able to assume normally distributed errors in probit models but not in linear probability models (LPM)? When used with a binary dependent variable, LPMs violate a few necessary ...
6
votes
4answers
161 views

Does leaving out an important predictor in a mixed linear model violate the independence assumption?

I have data from an experiment with 3 groups, measured at 4 time points, where each subject performed a task where 2 factors are manipulated: valence (3 levels) and predictability (2 levels). I know ...
2
votes
0answers
13 views

When creating a multiple regression model for a subgroup, is it necessary to test all assumptions again?

My results section consists of a multiple regression analysis considering 3 factors, containing all of my participants. Following this, I have considered males and females separately, by construction ...
3
votes
0answers
43 views

Controlling for individual in nlme when most individuals only measured once

I am trying to model growth using nlme for a number of individuals over four time periods. My question is, did growth differ over time? Some individuals were measured twice or more, perhaps as a young ...
0
votes
0answers
17 views

Are linear regression errors independent? Mean independent? Uncorrelated?

All I know is that we assume zero conditional mean (and hence zero mean) and conditional homoscedasticity (and hence homoscedasticity). When trying to prove that $E[(\hat{\beta_1} - \beta_1)\bar{u}] ...
0
votes
0answers
16 views

Assessing the residual independence assumption (nonlinear least squares regression diagnostics)

I would like to assess the assumptions underlying nonlinear regression models using statistical tests rather than graphical methods since I have thousands of fitting results. I am not certain ...
5
votes
1answer
72 views

When is it OK to write “we assumed a normal distribution” of an empirical measurement?

It is ingrained in the teaching of applied disciplines, such as medicine, that measurements of bio-medical quantities in the population follow a normal "bell curve." A Google search of the the string ...
3
votes
1answer
56 views

Is being the first-born independent of age?

If one were to assert that, in a large population, the fact of a person or an animal being the first-born in the family was independent of their age, then what assumptions (if any) would one have to ...
1
vote
1answer
45 views

Violation of proportional hazard for covariate but not for interaction it's part of in a Cox Proportional Hazards model

I have a problem in which one of the covariates in my model violates the assumption of proportional hazards, but the interaction it is part of does not. Data info: Lifespan - mosquito time to death ...
0
votes
1answer
26 views

Test of significant for success rate improvement on paired samples

I want to compare the success rate of two configurations of a program. A program run two times on the same set of photographs and returns each time a list of face matches. The success rate is ...
3
votes
1answer
130 views

Is Principal Component Analysis a parametric method?

Principal component analysis assumes that the features are distributed by a Gaussian. Does this make Principal Component Analysis a parametric approach? I can't seem to find a concrete answer saying ...
3
votes
2answers
108 views

Quadratic terms in logistic regression

I am looking at the results of a logistic regression model (i dont have the data) and the person who has developed the model has included quadratic terms in the model. I understand the use of such ...
1
vote
0answers
33 views

Multiple Regression Assumptions

This may seem like a basic question, but I'm verifying the assumptions for a multiple regression and have some trouble wrapping my head around homoscedasticity. I have a few questions listed below: ...
0
votes
0answers
21 views

ordination of non gaussian data

I'm trying to ordinate a quite big dataset (44 variables with scaled values between 0 and 1, with 800 observations), with evident correlations between them (both spearman and pearson pairwise r ...
0
votes
0answers
14 views

Which statistical assumptions are still important when fitting a GLM to > 1 million observations?

I have previously only fit GLM models to small/medium sized data (up to several thousand points, maybe tens of thousands). I always try to be meticulous about checking that GLM assumptions hold where ...
1
vote
0answers
17 views

Hypothesis testing hight z value

I am taking baby steps in statistics and after going through Hypothesis testing tutorials I took simple data set for try out. Date set here I will take you through my thinking and steps I took. I ...
2
votes
0answers
49 views

why does the same repeated measures anova using ezANOVA() vs. aov() yield different distributions of model residuals?

I am attempting to do a repeated measures anova using r with the aov() command from the {car} package. I wanted to be sure that I wrote my code for this approach correctly (see below), so I ...
0
votes
0answers
10 views

How do I test OLS assumptions with a multiple regression? [duplicate]

Trying to figure out how to test OLS assumptions with a multiple regression. Can anyone point me in the right direction or help me out? Working in R. This is for a school assignment, and I am allowed ...
0
votes
0answers
40 views

Are Gauss-Markov assumptions: $E(e_i|x_i) = 0$ and $cov(x_i, e_j)=0$ equivalent?

Is the assumption that: $E(e_i|x_i) = 0$ And the two assumptions that: $cov(x_i, e_i)=0$ $E(e_i)=0$ Equivalent? (I have seen both formulation of the assumptions). The two are certainly not ...
0
votes
0answers
11 views

Factor Analysis for non-normal data

The data I received from a newly derived scale appears to be not normal. Can I still conduct a factor analysis on this data or would I have to do something else to reduce the dimensions? Thanks,
1
vote
0answers
25 views

Residual non-normality and prediction intervals

Normal residuals are generally understood to be necessary for valid prediction intervals in OLS regression -- but I've never seen a definitive guidance on just how much non-normality can be tolerated, ...
0
votes
0answers
20 views

Hypothesis testing & inherently skewed data

I've run an experiment where I asked participants to indicate how they feel using a likert scale (1-7) in response to images they were being shown. The images were experimentally manipulated to ...
0
votes
0answers
80 views

Why does linear regression need mean independence?

If random variables $X$ and $U$ are independent, then they are mean independent by a rule of conditional expectation 'Pulling out independent factors'. If random variables $X$ and $U$ are mean ...
0
votes
0answers
30 views

Factorial ANCOVA: Independence of the covariate and treatment effects

I would like to test whether the assumptions of a 2x3 ANCOVA are met. How do I exactly test the assumption "independence of the covariate and treatment effects" in this case? I proceeded as follows: ...
0
votes
1answer
24 views

What kind of assumptions do I need to test when running a fixed effects panel model?

I am running a regression analysis on a panel data set. The Hausman test and the logical setup of the research question indicate that a fixed effects model would be best for running the regression. ...
1
vote
2answers
47 views

Appropriate test for comparison of 50+ groups

I am currently working with a data set containing several hundreds of thousands of instances for which I am trying to find the most appropriate analysis. The goal is to determine whether there are ...
1
vote
1answer
30 views

Checking model assumptions for a one-way ANOVA model with unequal sample sizes

Given an unbalanced completely randomized design with two treatment groups of unequal sizes and an appropriate one-way ANOVA parametric test, how can I properly check the validity of my model ...
0
votes
0answers
13 views

Regression: Finding meaningful counterexamples for a claim about implications of population rank conditions

Situation: In regression settings with stochastic regressor matrix $X$, one needs to impose assumptions on $X$ to validate inference. It is custom to specify these assumptions with respect to the ...
3
votes
0answers
12 views

reconciling Linearity and Multicollinearity assumptions in ANCOVA

For ANCOVA, many textbooks and other resources require, among other assumptions, that covariates be linearly related to the dependent variable, which makes sense. However, many of the same sources ...
0
votes
0answers
36 views

assumptions OLS regression met?

When I was checking the assumptions for a normal distribution these plots appeared: I was wondering what I could conclude from these... the dependent variable is winsorized between 0 and 1 ( and ...
1
vote
1answer
61 views

Survival Analysis: Cox Proportion Assumption

I know i'm missing something here, please help me understand the cox proportion assumption. What is the point of having a hazard rate function over time if it first has to meet the cox proportion ...
3
votes
1answer
53 views

ANOVA - Assumption of Independence

I am also a little bit confused about the assumption of independence. I have the following situation: one continuous dependent variable (radiant power of a medical device) a few independent ...
8
votes
2answers
196 views

Why is high positive kurtosis problematic for hypothesis tests?

I've heard (sorry cannot provide a link to a text, something I have been told) that a high positive kurtosis of residuals can be problematic for accurate hypothesis tests and confidence intervals (and ...
4
votes
1answer
291 views

What is the difference between these two Breusch-Pagan Tests?

Using R on some data and trying to see whether or not my data is heteroscedastic, I've found two implementations of the Breusch-Pagan test, bptest (package lmtest) and ncvTest (package car). However, ...
0
votes
1answer
36 views

Why 'Assumption of Uniformity' for Mean?

I am teaching some very basic statistics for grouped data. In a different class a few years ago I made an 'assumption of uniformity' about the data: within bins, data is uniformly distributed ...
21
votes
1answer
857 views

How incorrect is a regression model when assumptions are not met?

When fitting a regression model, what happens if the assumptions of the outputs are not met, specifically: What happens if the residuals are not homoscedastic? If the residuals show an increasing or ...
3
votes
1answer
121 views

Multicollinearity in simple linear regression (not multiple)?

I am doing a simple linear regression analysis with 1 independent variable. I am checking data against assumptions. As I am checking against Tolerance and VIF level, I get the their values equal to 1 ...
3
votes
2answers
66 views

Uses and estimation of Student-t distribution

My question relates to a confusion between how the Student-t distribution is often documented versus how it is used. In the documentation the Student-t is used (from Wikipedia): when estimating ...