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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2
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1answer
28 views

If my normality test is non-significant, am I safe to use the t-test?

I took a 30 unit sample from a population. The sample distribution resulted to be normal. Can I state that the population distribution is normal too? If so, with what level of confidence?
2
votes
0answers
26 views

Objective test for proportionality assumption in Cox Regression Model (SAS)?

I was trying to fit Cox Regression (aka Proportional Hazard) model on some cancer data (N=2288). I got the following output from SAS proc phreg: ...
2
votes
1answer
31 views

What’s wrong with this way of fitting time-dependent coefficients in a Cox regression?

I have a Cox proportional hazards model. Judging by Schoenfeld residual vs. time plots and corresponding tests for zero slope, there is clear violation of the PH assumption for several of the ...
3
votes
0answers
27 views

Partial regression plots vs scatter plots for checking linearity

In a multiple linear regression analysis, what is the most suitable plot for checking linearity? I have seen a number of examples that use scatterplots as a preliminary test to use a linear model. ...
3
votes
0answers
38 views

Significant output in Levene's test for equality of variances in MANOVA; what to do?

I want to perform a MANOVA in SPSS as follows: i have a independent variable that consists of three groups (- of which the group sizes are not equal), and six dependent variables. When checking the ...
0
votes
0answers
12 views

web analytic: average session length based on visits numbers per minute

At hand, I only have the number of visits per minute for one web page (one day period), is it possible to generate the average session length of such page based on these numbers? Or and references I ...
1
vote
1answer
18 views

Assumptions of two way anova

Please tell me what the actual assumptions of a two way anova are. I read somewhere that this being similar to multiple regression, the only assumption is that the residuals are to be normally ...
4
votes
1answer
65 views

Working with residuals of regression

So the background is that the I collected yield data for past 5-6 decades and location from where I collected yield data had high yielding varieties introduced over time. I am looking at the ...
1
vote
1answer
36 views

Is it necessary to plot histogram of dependent variable before running simple linear regression?

I was working on an assignment. The data set was really simple, only consisting one independent variable $y$ and dependent variable $x$. Someone suggested me plot a histogram of $y$ before running ...
1
vote
0answers
19 views

How do I test for homogeneity of regression in MANCOVA?

I am using a one-way independent MANCOVA with 4 dependent variables and a single covariate. My predictor is bivariate. I'm currently trying to test for heterogeneity of regression in SPSS by ...
4
votes
2answers
71 views

Two simple questions regarding GLM

I'm currently doing a modelling project. However, I haven't taken a bunch of statistics classes, so I have to teach myself generalized linear models. I'm reading Generalized Linear Models for ...
2
votes
1answer
31 views

Assumption for valid hypothesis testing of the OLS estimators in the small samples

Please support me solve this question: In a simple regression model y = b0 + b1*x + u we have the five main assumptions 1 linearity in parameters 2 random sampling 3 zero conditional mean 4 variation ...
1
vote
1answer
37 views

R - Test for homogeneity of regression slopes results in singular model

I am trying to check the assumptions of a two-way ANCOVA. So in my model I have two factors (F1, F2) one dummy coded two level covariate (C) one dependent variable (D) In order to check the ...
0
votes
0answers
23 views

How to check discriminant analysis assumption in R using lda?

I want to use lda in MASS package in R. According to the theory behind that, first need to validate the assumption. Actually I've found some example from the net but they did not bother to validate ...
4
votes
1answer
64 views

2x2 ANOVA - assess violations of homoscedasticity & normality

I have a 2x2 factorial unbalanced between-subject design, n = 355. My DV is a subjective probability estimate (i.e., a number between 0 and 100). My ANOVA model: ...
6
votes
2answers
149 views

Heteroskedasticity in residuals vs. fitted plot

I am testing whether price per ounce of beer (continuous variable, range of values mostly between 0.1 and 0.5 dollars) and the presence of promotion, advertisement, and display (all binary) have ...
10
votes
2answers
289 views

Are normally distributed X and Y more likely to result in normally distributed residuals?

Here the misinterpretation of the assumption of normality in linear regression is discussed (that the 'normality' refers the the X and/or Y rather than the residuals), and the poster asks if it is ...
3
votes
2answers
178 views

Logistic regression assumptions for a model with many binary independent variables

I am working on developing a logistic regression model that uses qualitative variables only ($n=990$). My remit is to define the equation that can identify the most relevant characteristics of a ...
4
votes
0answers
51 views

Model assumptions of partial least squares (PLS)

I am trying to find information regarding the assumptions of PLS regression (single Y). I am especially interested in a comparison of the assumptions of PLS with regards to those of OLS regression. ...
0
votes
0answers
24 views

Discarding the violation of homogeneity of variance in ANOVA

If after a bartlett.test(split(x,list(f1,f2))) I get a very significant p-value, but my sample size is huge -- millions of measurements -- is it justified to ...
0
votes
1answer
36 views

When reporting a CFA, is it desirable to assess univariate normality in addition to multivariate normality?

I am using SPSS AMOS to do a factor analysis and it produces statistics related to both univariate and multivariate normality. p35 of the AMOS Users Guide states that "[T]he observed variables must ...
5
votes
1answer
168 views

Does testing for assumptions affect type I error?

I just performed simple simulation. Made two "populations" with different means and the same variance. Since I prepared them I know that they: are normal, differs in location and both have the same ...
4
votes
3answers
269 views

Assumptions of linear models and what to do if the residuals are not normally distributed

I am a little bit confused on what the assumptions of linear regression are. So far I checked whether: all of the explanatory variables correlated linearly with the response variable. (This was the ...
2
votes
1answer
63 views

Do these residual plots violate the linearity and homogeneity assumptions for linear regression?

There seem to be too many points clustered around negative values for all the plots And while 3 & 4 seem to have random enough patterns, 1 & 2 seems to have negatively sloped trend. If ...
1
vote
1answer
90 views

Why does poisson regression need to assume observations are poisson distributed?

Zuur (2013) 'Beginners Guide to GLM and GLMM' states that if the Pearson residuals, when plotted against fitted values from a poisson regression, show the pattern below then the assumption of poisson ...
2
votes
1answer
83 views

Consequences of non normal-distribution multiple regression

I am currently writing my thesis using multiple regression. However my data does not meet the regression assumption of normal distribution. I want to describe that, due to the non normal ...
0
votes
1answer
98 views

What makes the results differ for fixed-effects models vis-à-vis random effects models?

What makes the results differ for fixed-effects models vis-à-vis random effects models? The Cochrane Collaboration's website indicated that two models can produce different results for a meta ...
10
votes
4answers
394 views

Practically speaking, how do people handle ANOVA when the data doesn't quite meet assumptions?

This isn't a strictly stats question--I can read all the textbooks about ANOVA assumptions--I'm trying to figure out how actual working analysts handle data that doesn't quite meet the assumptions. ...
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0answers
35 views

Forecast accuracy – can we use correlation and $t$-tests?

Does it make sense to compare actual vs. forecast using correlation analysis / see how close $R^2$ is to 1? Does it make sense to use a paired t-test to test actual vs. forecast to get accuracy of ...
0
votes
3answers
101 views

Is it compulsory for a linear regression analysis that a dependent as well as independent variable have equal variance?

The literature suggests that we need to have dataset that meets the condition of homoscedasticity. However, it seems that such a condition is not proper.
1
vote
2answers
84 views

Percent error for linear regression model

Suppose I fit a linear regression $y = \beta x + \rm error$. In this situation, $x > 0$, $\alpha > 0$, and therefore $y > 0$. Moreover, the $\rm error$ is normally distributed with mean $0$ ...
3
votes
1answer
27 views

Validating assumed distributions in parametric models

When using a model that assumes a specific distribution of data, I get confused about how seriously I need to check for the assumption. For example, if we use some statistical test (e.g., based on ...
2
votes
1answer
66 views

Is it ok to spit non-normal variables in tertiles and put them into multivariate regression models?

I am now reviewing a paper in which the authors decided to predict a DV through linear regression using, beyond other variables, dummy variables obtained from a tertile split of continuous variables, ...
1
vote
0answers
35 views

Can mean values be seen as a form of PCA with a LOT of assumptions?

This question just occurred to me out of the blue. PCA is a way to reduce dimensions. Another way that is often (perhaps too often) used is to take mean values of two or more variables. This is done a ...
2
votes
1answer
99 views

Mann-Whitney test

I am carrying out a study in which I have $3$ groups $n= 2$, $n= 5$, $n=17$. Each group has a different inner ear abnormality, following the fitting of an ear implant I have measured their speech ...
6
votes
2answers
494 views

Variance-Covariance matrix interpretation

Assume we have a linear model Model1 and vcov(Model1) gives the following matrix: ...
2
votes
3answers
115 views

What to do when I have expected count <5 warning for a chi squared test?

I applied a survey consisting of 12 questions to 120 people and each questions include 4 nominal categories; I want to make comparison of people's answers according to their socio-demographic ...
0
votes
1answer
44 views

T test assumptions

I'm performing a t-test on a time series with a sliding window (i.e. every N samples, perform a t test). I know that overall, the samples are roughly normally distributed, however adjacent samples ...
2
votes
2answers
95 views

Same dataset analysed with four different linear models

I've analysed the same dataset (diamonds from ggplot2) in R with four linear models. Each model has a different error structure. ...
3
votes
2answers
275 views

Checking residuals for normality in generalised linear models

This paper uses generalised linear models (both binomial and negative binomial error distributions) to analyse data. But then in the statistical analysis section of the methods, there is this ...
1
vote
0answers
38 views

Forecasting and auto-correlation [duplicate]

I'm reading this chapter forecasting principles and practise from a forecasting book. The author has explained a linear regression model. Now this linear regression model will definitely have some ...
0
votes
0answers
50 views

Cross correlation in Stata

I have a cross sectional dataset of investment figures (in $) and a set of dependent variables. The data relates to various years (2000-2013). More than one investment is included in the dataset for ...
2
votes
0answers
22 views

Ways the likelihood ratio test may fail

I have a question related to: Why is a likelihood-ratio test distributed chi-squared? On point 2 of @StasK's answer, he states: The theorem assumes that all the relevant derivatives are ...
5
votes
1answer
295 views

Is it ever okay to ignore heteroskedastic residuals and continue with analysis?

My data is misbehaving and I can't seem to get residuals with constant variance despite doing more transformations than Optimus Prime. Is it ever okay to just continue with analysis in and just make a ...
4
votes
1answer
334 views

What is the minimum viable cell size for 2x2 ANOVA?

I have a 2x2, between-subjects experimental design (2 independent variables (IVs) with 2 levels each) and one dependent variable (DV). My data are unbalanced and an interaction between the IVs seems ...
2
votes
1answer
55 views

residuals plots from four linear models

I've made 4 linear models. For each of these models, I've plotted the residuals against the fitted values. First plot: generalised linear model with quasibinomial link function Second ...
0
votes
0answers
88 views

Fit of negative binomial regression model

I have ran a negative binomial regression. I'm guessing the use of a negative binomial regression is not ideal given my design, but I'm hoping I can 'get away with it', as it seems to be working ...
1
vote
1answer
71 views

What are the important conditions in ANOVA fixed effects?

I am working with an ANOVA model. I want to run a fixed effects ANOVA in which I have a ratio dependent variable and three independent variables with two and three levels. Obviously, before analyzing ...
5
votes
0answers
74 views

How can one test the assumptions of a zero-inflated negative binomial model in R?

I have fitted a zero-inflated model with a random effect using a negative binomial distribution in R, using the function glmmadmb. This is due to a large number of zeros and over dispersion. For a ...
4
votes
1answer
162 views

Normality assumption in linear regression

As an assumption of linear regression, the normality of the distribution of the error is sometimes wrongly "extended" or interpreted as the need for normality of the y or x. Is it possible to ...