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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1answer
7 views

What are the assumptions to path analysis and how to test them?

There are some structural assumptions to path analysis that are not difficult ascertain. They are (a) no loops (b) no going forward and backward (c) a maximum of one curved arrow per path. I am aware ...
1
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0answers
18 views

Which assumptions do I need to check for a GLMM with a binary response (and how?)

I am modeling binomial responses using Generalized Linear Mixed Models with a nested random effect (not of interest, simply a control: year nested within location) and both categorical, count, and ...
1
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0answers
12 views

Assumptions for PCR and PLS

I am writing up a report on fitting Principal Component Regression (PCR) and Partial Least Squares (PLS) to my data-set. A similar question: Model assumptions of partial least squares (PLS) ...
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0answers
7 views

Independence of errors in fixed effects regression

I am trying to run a fixed effects regression and am currently testing assumptions. This probably is rather a "beginner question", but how do I test the assumption of independence of errors for ...
1
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1answer
30 views

Performing a t-test with discrete (currency) data

I want to perform a 2 sample t-test assuming unequal variances, however my variable is currency. Currency is discrete, however when checking the assumptions of the t-test, I see that the data should ...
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0answers
4 views

Greenhouse-Geisser correction vs Non-independence

In repeated-measure ANOVA, Greenhouse-Geisser correction is commonly used to handle sphericity issues (when there are more than three levels in a condition). But does it deal with data that violate ...
7
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1answer
185 views

Why does including $x\ln(x)$ interaction term in logistic regression model helps to assess linearity assumption?

In Discovering Statistics using SPSS 4th Edition by Andy Field, it was recommend to include the interaction term between the independent variable $x$ and its corresponding natural logarithm transform $...
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0answers
16 views

Proportional odds assumption for grouped data with known thresholds

I have a question regarding ordinal logit model. My dependent variable, y, is grouped by known thresholds. For example, 1, 2 and 3 define income less than $ 9,999, income between 10000-39,999, and ...
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1answer
24 views

Does Naive Bayes assume normality?

I came across this paper about Naive Bayes that states [Naive Bayes] is based on another common simplifying assumption: the values of numeric attributes are normally distributed within each class. ...
2
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0answers
46 views

ANCOVA for 2X2 crossover with baseline measurements - validity of assumptions

I'm trying to learn how to do ANCOVA for a 2x2 crossover study with baseline measurements. I have followed the analysis performed at Mehrotra 2014 "A recommended analysis for 2 x 2 crossover trials ...
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0answers
12 views

Using raw residuals rather than standardized residuals for Q-Q/P-P plot and residual vs fitted plot

When I was doing my undergraduate studies, I remembered that my lecturers usually uses raw residuals to obtain the plots mentioned above. Some books that I have also used raw residuals. However, ...
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1answer
13 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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0answers
20 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
22 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 ...
2
votes
1answer
52 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 ...
2
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1answer
35 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
27 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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0answers
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 two-...
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0answers
27 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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0answers
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 question.)...
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1answer
91 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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0answers
19 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
7 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 ...
10
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2answers
332 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. It'...
3
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0answers
36 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 ...
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3answers
128 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
50 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
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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
79 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
101 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
83 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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0answers
13 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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0answers
37 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 ...
10
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3answers
670 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 real-...
11
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3answers
584 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
71 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 assumptions....
6
votes
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
20 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
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0answers
50 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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0answers
23 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
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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
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1answer
79 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
64 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
35 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
138 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
125 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
39 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: 1)...