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 ...

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3 views

Checking linearity assumption and colinearity assumption

Linear regression is equivalent to ANOVA. Do we need to check for linearity and multicolinearity for ANOVA? I have seen that these assumptions are usually omitted for ANOVA but not for linear ...
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11 views

Measure relationship between continuous variable & unbalanced binary variable

I am trying to select variables for modelling a binary variable (whether a person will repay a loan) using various continuous variables about them - age, income, years of education, etc. I'd like to ...
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0answers
17 views

VIF interactions

I would like to check for multicollinearity in logistic regression analysis. Independent variables are categorical (always binary) and continuous. Sample has limitted size (N=176, 36 events), so I can ...
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1answer
43 views

Can logistic regression be used with “years” as a continuous variable?

We are currently collecting data for a study whose purpose is to show whether scientists are focusing more or less on a specific subject with time. To keep some privacy let's say the subject is jelly ...
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0answers
58 views

Standard error explanation [on hold]

In which direction and how far away, in terms of standard errors, is the estimated  difference ( Diff x ) in Case 1 from the hypothesised value. So what I've done is calculated t0= estimate - ...
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1answer
14 views

How to check permutation testing exchangeability assumption when using a General Linear Model

I have a question on the assumption of exchangeability in permutation tests. Although I read a lot about this topic, I am still confused. For $N$ subjects, I have the value of a clinical measure $Y$ ...
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1answer
22 views

Fisher and chi-squared assumptions/limitations not met

Fisher exact test is said to be used with a total sample (n) < 1000, whereas chi-squared test should be used when each category (/cell in a contingency table) >=5. What if you have an mxn ...
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9 views

Why do we need to check for these two assumptions for ANCOVA but not for factorial ANOVA?

ANCOVA has two additional assumptions as compared to two-way factorial ANOVA. They are (1) independence of the covariate and factor (2) homogeneity of slope. Why don't we need to check them for ...
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0answers
25 views

Does the unconditional distribution of $y_i$ only depend on the distribution of the errors?

In linear regression, does the unconditional distribution of $y_i$ only depend on the distribution of the errors? For example, is it not the case that if $$y_i = \beta_0 +\beta_1 x_i + u_i $$ and ...
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36 views

predictor skewed but normality of errors

In linear regression (linearity assumption had been checked), what is the effect if distribution of predictor is skewed but errors are normally distributed? Is there a risk for estimation of ...
2
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0answers
27 views

Why is the “sphericity assumption” in RM-ANOVA (constant variance of difference scores) called “sphericity”?

Why is the "sphericity assumption" in RM-ANOVA, i.e. the assumption of constant variance of difference scores, called "sphericity"? (This question was suggested in the comments to a related ...
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1answer
79 views

help for interpreting the residuals vs. fitted values plot

I have an ordinal variable (scale of stress) considered as continuous predictor in a multivariate linear regression. I would like to verify the assumption of linearity and this is my residuals vs. ...
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11 views

In testing Heteroscedasticity, when should I use Park Test or Glejser Test?

I am currently running a data analysis on survey data. In testing the heteroscedasticity assumption in Multiple Linear Regression, using Park Test, the research actually passed the test. However, when ...
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0answers
21 views

When are observations not weakly exchangeable?

In the book "Common errors in statistics", I read the following statement Permutation tests only yield exact significance levels if the labels on the observations are weakly exchangeable under ...
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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 ...
5
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2answers
176 views

Regression: why test normality of overall residuals, instead of residuals conditional on $\hat{y}$?

I understand that in linear regression the errors are assumed to be normally distributed, conditional on the predicted value of y. Then we look at the residuals as a kind of proxy for the errors. ...
3
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0answers
25 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
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3answers
123 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 ...
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0answers
46 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 ...
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0answers
31 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
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1answer
68 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 ...
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2answers
87 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 ...
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1answer
78 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)} ...
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11 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, ...
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36 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 ...
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3answers
658 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 ...
11
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3answers
577 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 ...
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1answer
57 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
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4answers
162 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
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0answers
16 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 ...
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0answers
46 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 ...
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22 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}] ...
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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
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1answer
75 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
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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
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1answer
55 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
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1answer
28 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
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1answer
134 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
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2answers
112 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
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0answers
37 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: ...
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0answers
22 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 ...
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0answers
17 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 ...
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0answers
19 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
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0answers
61 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 ...
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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
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0answers
42 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 ...
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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,
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31 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, ...
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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 ...
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0answers
82 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 ...