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I don't understand why not ? You can use BMI as a continuous variable and introduce an interaction variable with age group. For example Y = Intercept + Beta1*BMI +Beta2*BMI*(Age Category = Adolescents) + Beta3*BMI*(Age Category = Adult) This model would adjust BMI coefficient according to age...

Okay. If you are okay with it no problem.

Hello, I can help you about the interpretation but why do you include both obese and overweight at the same time. Couldn't you use only one variable including reference, overweight and obese. This would make better sense. For a female obese: Y = 4.4004 + 0.041*ethnicity - 0.071*age -...

Okay, I'm not sure what that really means. Odds ratio can be calculated from contingency tables as you put in the second photo. But I'm also sure that it can be calculated via logistic regression analysis. There should be a relationship between them but I don't know really. I will try to do...

To give odds you should do logistic regression. With chi or fisher you can only report significant frequency difference between groups with regards to your findings. Chi and fisher doesn't give Odds.

Yes I mean a chi-square test for every finding. To run a Chi-square test your data is adequate. Expected values are intrinsically calculated in chi-square formula based on your contingency table, so no, you don't need to provide extra expected characteristics data, chi-square test takes care of...

Hello, I think you should do repeated Chi-square tests (fisher if needed) for each outcome. For example fining A present/absent vs controls/cases. This will simply show if the frequency of your findings differ between cases and controls. BUT! I wouldn't stop there. Seeing the predictive value...

Hey, Question asks the possibility of tossing the dice two times before tossing blue, in other words it asks. 1(all possible tosses) - the possibility of tossing the dice blue before the third toss. In this case there are 2 possibilities. You can toss the dice blue first and stop, the second...

This article by Hripcsak and Heitjanb tackles many issues I came up to when doing work about reliability analyses is good help. It also includes tetrachoric correlation discussion as well. https://core.ac.uk/download/pdf/82193386.pdf

I think tetrachoric correlation may be a solution for me.

please add the questions

Wow! Good source indeed. I failed to find any correlation that takes care of binary-binary cases but still thanks!

1. Why not repeated measures t-test but anova? How many subject groups will you have, how many repeats you have (I guess 2)) 2. 2 linear regression models: one with LoC and the other with neuroticism. Than you can compare adjusted-r scores, if both estimates are significant. 3. Linear regression...

Only one rater did two ratings in different time points. Correlation coefficient wouldn't work in my case since I have binary variable with only 0 and 1. If this was some kind of continuous variable I would use ICC (intra-class coefficient) and it would cause no problem. In my example one of...

Okay then paired-sample t-test is fine. Why do you think this is a limitation. Actually measuring the result from an unchanged sample can be a strength of your study. If the samples were different your would be less reliable at showing the change in mean_css. Different sample would introduce new...

Difference is significant (p = 0.001) and there is increase in mean_css (0.2167). Did your sample change from 2019 to 2021 or are they the same? If they are the same sample getting the same scale than you should the paired-samples-test. If not independent samples t-test is more appropriate.

T-test would tell if there is difference in means of given variables with regards to wine types. I agree that the question is not well posed, but still t-test would be a good start.

I agree. I had many problems using it. What do you mean by marginals are fixed?

Haha, you are right. Intra-rater reliability refers to measurements of one measurer on different time points. My results means that the measurer gave the same values for the same variable in both time points. This is possible since the measurement is easy and the variable is binary. I agree...

For the first part I would use t-tests and for the second part 7 univariate logistic regressions and then multivariate regression with the significant variables on the first 7 univariate regressions.