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Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

I wonder how to better understand their difference (of using z-value vs t-value) more mathematically?

Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

I saw somewhere (as well from the comment below) that the reason is: the dispersion parameter for the linear model is $\sigma^2$ while the dispersion parameter is 1 for the logistic/poisson regression. If this is so, I would like to understand more mathematically why the value of this dispersion parameter leads to using z-values or t-values.

Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

Intuitively, I guess it may be due to that in linear regression, variance and mean are independent, but in logistic/poisson regression, they are dependent. If so, I wonder how to better understand their difference (of using z-value vs t-value) more mathematically?

Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

I wonder how to better understand their difference (of using z-value vs t-value) more mathematically?

Why does logistic/poisson regression in R give z values while linear regression gives t values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context.

Intuitively, I guess it may be due to that in linear regression, variance and mean are independent, but in logistic/poisson regression, they are dependent. If so, I wonder how to better understand their difference (of using z-value vs t-value) more mathematically?