#γ — Public Fediverse posts

"A generalized linear model or #GLM consists of three components:
1. A random component, specifying the conditional distribution of the response variable, Yᵢ (for the ith of n independently sampled observations). […]
2. A linear predictor—that is a linear function of regressors,
ηᵢ = α + Σⱼ Xᵢⱼ*βⱼ
3. A smooth and invertible link function g(·), which transforms the expectation of the response variable, μᵢ ≡ E(Yᵢ), to the linear predictor:
g(μᵢ) = ηᵢ"

https://www.sagepub.com/sites/default/files/upm-binaries/21121_Chapter_15.pdf

#models #dataDev #logNormal #regression #normality #normalDistribution #gamma #Γ #modelling #modeling #AIDev #ML #evaluation

"A generalized linear model or #GLM consists of three components:
1. A random component, specifying the conditional distribution of the response variable, Yᵢ (for the ith of n independently sampled observations). […]
2. A linear predictor—that is a linear function of regressors,
ηᵢ = α + Σⱼ Xᵢⱼ*βⱼ
3. A smooth and invertible link function g(·), which transforms the expectation of the response variable, μᵢ ≡ E(Yᵢ), to the linear predictor:
g(μᵢ) = ηᵢ"

https://www.sagepub.com/sites/default/files/upm-binaries/21121_Chapter_15.pdf

#models #dataDev #logNormal #regression #normality #normalDistribution #gamma #Γ #modelling #modeling #AIDev #ML #evaluation

"A generalized linear model or #GLM consists of three components:
1. A random component, specifying the conditional distribution of the response variable, Yᵢ (for the ith of n independently sampled observations). […]
2. A linear predictor—that is a linear function of regressors,
ηᵢ = α + Σⱼ Xᵢⱼ*βⱼ
3. A smooth and invertible link function g(·), which transforms the expectation of the response variable, μᵢ ≡ E(Yᵢ), to the linear predictor:
g(μᵢ) = ηᵢ"

https://www.sagepub.com/sites/default/files/upm-binaries/21121_Chapter_15.pdf

#models #dataDev #logNormal #regression #normality #normalDistribution #gamma #Γ #modelling #modeling #AIDev #ML #evaluation

"A generalized linear model or #GLM consists of three components:
1. A random component, specifying the conditional distribution of the response variable, Yᵢ (for the ith of n independently sampled observations). […]
2. A linear predictor—that is a linear function of regressors,
ηᵢ = α + Σⱼ Xᵢⱼ*βⱼ
3. A smooth and invertible link function g(·), which transforms the expectation of the response variable, μᵢ ≡ E(Yᵢ), to the linear predictor:
g(μᵢ) = ηᵢ"

https://www.sagepub.com/sites/default/files/upm-binaries/21121_Chapter_15.pdf

#models #dataDev #logNormal #regression #normality #normalDistribution #gamma #Γ #modelling #modeling #AIDev #ML #evaluation

"A generalized linear model or #GLM consists of three components:
1. A random component, specifying the conditional distribution of the response variable, Yᵢ (for the ith of n independently sampled observations). […]
2. A linear predictor—that is a linear function of regressors,
ηᵢ = α + Σⱼ Xᵢⱼ*βⱼ
3. A smooth and invertible link function g(·), which transforms the expectation of the response variable, μᵢ ≡ E(Yᵢ), to the linear predictor:
g(μᵢ) = ηᵢ"

https://www.sagepub.com/sites/default/files/upm-binaries/21121_Chapter_15.pdf

#models #dataDev #logNormal #regression #normality #normalDistribution #gamma #Γ #modelling #modeling #AIDev #ML #evaluation

"The #gamma GLM is a relatively assumption-light means of #modeling non-negative data, given gamma's flexibility.
[…]
"Explaining what is used and what is not used, despite merits and demerits […]: Loosely, the larger the internal literature in any field on modelling techniques, the less inclined people in that field seem to be to try something different."

Nick Cox, 2013: https://stats.stackexchange.com/questions/67547/when-to-use-gamma-glms

#normality #normalDistribution #Γ #modelling #dataDev #AIDev #ML #AIEvaluation #logNormal

"The #gamma GLM is a relatively assumption-light means of #modeling non-negative data, given gamma's flexibility.
[…]
"Explaining what is used and what is not used, despite merits and demerits […]: Loosely, the larger the internal literature in any field on modelling techniques, the less inclined people in that field seem to be to try something different."

Nick Cox, 2013: https://stats.stackexchange.com/questions/67547/when-to-use-gamma-glms

#normality #normalDistribution #Γ #modelling #dataDev #AIDev #ML #AIEvaluation #logNormal

"The #gamma GLM is a relatively assumption-light means of #modeling non-negative data, given gamma's flexibility.
[…]
"Explaining what is used and what is not used, despite merits and demerits […]: Loosely, the larger the internal literature in any field on modelling techniques, the less inclined people in that field seem to be to try something different."

Nick Cox, 2013: https://stats.stackexchange.com/questions/67547/when-to-use-gamma-glms

#normality #normalDistribution #Γ #modelling #dataDev #AIDev #ML #AIEvaluation #logNormal

"The #gamma GLM is a relatively assumption-light means of #modeling non-negative data, given gamma's flexibility.
[…]
"Explaining what is used and what is not used, despite merits and demerits […]: Loosely, the larger the internal literature in any field on modelling techniques, the less inclined people in that field seem to be to try something different."

Nick Cox, 2013: https://stats.stackexchange.com/questions/67547/when-to-use-gamma-glms

#normality #normalDistribution #Γ #modelling #dataDev #AIDev #ML #AIEvaluation #logNormal

"The #gamma GLM is a relatively assumption-light means of #modeling non-negative data, given gamma's flexibility.
[…]
"Explaining what is used and what is not used, despite merits and demerits […]: Loosely, the larger the internal literature in any field on modelling techniques, the less inclined people in that field seem to be to try something different."

Nick Cox, 2013: https://stats.stackexchange.com/questions/67547/when-to-use-gamma-glms

#normality #normalDistribution #Γ #modelling #dataDev #AIDev #ML #AIEvaluation #logNormal