Here you can find analysis using various regression approaches such as linear and multiple linear models, generalized linear models and linear mixed models.
- GLM & LMM: GLMs with the poission and binomial link function, as well one LLM implemented in R.
- LMM_politeness.rmd analyses what explanatory variables may be used to predict and understand our voice frequency
- GLM_poisson_soaysheepfitness.rmd using the logit link function in a glm to analyse predictors for the fertility of soay sheep.
- GLM_binomial_pesticides.rmd uses the binmial family link in a glm to find how different pesticides affect an insect.
- Linear Regression: Linear and multiple regression implemented in python and R, as well as outlier analysis.
- migrating_birds.ipynb analyses the relationship of bird features with their migrating distance in winter.
- LM_dicrete_earthworm.rmd finds different relationships between distinguished earthworm species of weight with gut circumference.
- LM_continuous_bodyfat.rmd explores which features of human physique can predict bodyfat.
- outlier-leverage.ipynb simplistic search for outliers and leverage point and how to handle them accordingly
- Model Selection: Performing model selection of linear model (lm) in R according to AIC.
- predicitve_model_of_bodyfat.R: manual model selection by stepwise dropping of explanatory variables according to AIC to obtain optimal model to predict bodyfat.
-model-dredge-echocardiogram.rmd: automated model selection with the
dredgefunction from the "MuMIn" package to predict the probability to survide heart attacks
- predicitve_model_of_bodyfat.R: manual model selection by stepwise dropping of explanatory variables according to AIC to obtain optimal model to predict bodyfat.
-model-dredge-echocardiogram.rmd: automated model selection with the