# Tag Info

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Categorical variables can be represented several different ways in a regression model. The most common, by far, is reference cell coding. From your description (and my prior), I suspect that is what was used in your case. The standard statistical output will give you two tests. Let's say that A is the reference level, you will have a test of B vs. A, and ...

4

As already pointed out in my comment referring to the original question, your preferred null hypothesis "color distribution in Urn 1 is equal to color distribution in all Urns combined" is equivalent to the null hypothesis "color distribution in Urn 1 is equal to color distribution in Urn 2-7". The former recycles observations in Urn 1, destroying ...

2

The chisquare is a hypothesis test for differences from independence in the counts in your table. If you want to test that you're probably not doing anything wrong. You can produce a table of contribution to chi-square or a table of Pearson residuals which help to identify which parts of the table contribute most to the differences. However, it sounds ...

2

You correctly performed a $\chi^2$-test of independence, so the only problem is in the formulation of its hypotheses and the interpretation of the test result: The $\chi^2$-test of independence tests the null hypothesis "The two color distributions are equal" versus the working hypothesis of any difference. The p value is smaller than the prespecified level ...

1

Regardless of what level you set as reference, the resulting model fit will be equivalent. You are probably interested in if there are there are mean differences in the substrate levels. After fitting the model you should run a Tukey Post-Hoc comparison to see which levels differ. TukeyHSD function in R. Again, it does not matter on what the reference ...

1

I'll answer the question in the title even though I think noone should ever do what I am about to describe. @Emma it is good you came to this site, you should ask instead what is the best way to compare multiple categories of likert scale data. Also you should search for information about what is special about the number 0.05. I knew nothing about ...

1

You could check out Gertheiss & Tutz, Penalized Regression with Ordinal Predictors, & their R package ordPens. They say:– Rather than estimating the parameters by simple maximum likelihood methods we propose to penalize differences between coefficients of adjacent categories in the estimation procedure. The rationale behind is as follows: ...

1

I don't think you need variances covariances; I think you simply define a measure of selectivity. This could be any of various things - I don't know of a general agreement on this. Some ideas: Max proportion - minimum proportion Sum of absolute deviations from equal proportions Just the maximum proportion Any of these scaled by number of categories. One ...

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I think you probably want a regression model. Surely mortality is a dependent variable here, and it seems like you want to examine the effect of race on it. What sort of regression model depends on how mortality is recorded. It could be a (regular) logistic regression, an ordered logistic or possibly a survival model. As for a simple display, it seems ...

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First off, are your two independent variables being adjusted as factors or numerically coded responses and is there an interaction term for the two? The reason I ask is because the test of proportional odds grows very sensitive with small cell counts. For this reason, I often find it justifiable to adjust input variables as their ordinally coded values (1: ...

1

Leaving aside the appropriateness of using a 0-1 variable as the response (dependent variable) in a linear regression (about which see gung's answer): It makes no difference whether you treat a 0-1 variable as categorical or numeric. To make a numeric variable categorical in R, you would as.factor them, and then the dummy for the second level of the ...

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Given that your response variable y is categorical, it would violate the assumptions of standard linear (OLS) regression, if you were to use that instead of logistic regression. Specifically, the residuals would not be normally distributed and the residual variance would not be constant (since the variance of a proportion is a function of the proportion ...

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