"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

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions