jamovi

Hi!

In the frequency tab, we have a log-linear regression, to compare multiple categorical variables!
I understand the conditions (requirements) to use/do this analysis, however I lack how to interpret one of this… I did not find any video or blog explaining clearly this!

Please, if someone could post some images here with an interpretation of the results, that would help!
I mean, what should we first look for, then what should we do to improve the model? Is there any way to obtain some graphics? (maybe with some R package?)

Thanks!!!

from https://data.library.virginia.edu/inter ... ear-model/

Only the dependent/response variable is log-transformed.
Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or decrease) in the response for every one-unit increase in the independent variable.
Example: the coefficient is 0.198. (exp(0.198) – 1) * 100 = 21.9. For every one-unit increase in the independent variable, our dependent variable increases by about 22%.
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Only independent/predictor variable(s) is log-transformed.
Divide the coefficient by 100. This tells us that a 1% increase in the independent variable increases (or decreases) the dependent variable by (coefficient/100) units.
Example: the coefficient is 0.198. 0.198/100 = 0.00198. For every 1% increase in the independent variable, our dependent variable increases by about 0.002. For x percent increase, multiply the coefficient by log(1.x). Example: For every 10% increase in the independent variable, our dependent variable increases by about 0.198 * log(1.10) = 0.02.
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Both dependent/response variable and independent/predictor variable(s) are log-transformed.
Interpret the coefficient as the percent increase in the dependent variable for every 1% increase in the independent variable.
Example: the coefficient is 0.198. For every 1% increase in the independent variable, our dependent variable increases by about 0.20%. For x percent increase, calculate 1.x to the power of the coefficient, subtract 1, and multiply by 100. Example: For every 20% increase in the independent variable, our dependent variable increases by about (1.20 0.198 – 1) * 100 = 3.7 percent.