Here are some of the resources that I found helpful for writing up our limitations section and I thought I would share it with you in the case that it may of interest.
A summary of the assumptions of linear regression here and how to diagnose violations here. It would be helpful to explicitly test exactly which assumptions are being violated so we can adopt the correct model.
Polynomial regression is helpful for remedying assumption of linearity. Another way to remedy the violation of linearity is to try transforming our raw data.
Generalized linear model with a poisson or negative binomial link function. Since our response variable is a count a distribution like the poisson or negative binomial are a better fit.
Hey team,
Here are some of the resources that I found helpful for writing up our limitations section and I thought I would share it with you in the case that it may of interest.
A summary of the assumptions of linear regression here and how to diagnose violations here. It would be helpful to explicitly test exactly which assumptions are being violated so we can adopt the correct model.
Polynomial regression is helpful for remedying assumption of linearity. Another way to remedy the violation of linearity is to try transforming our raw data.
Generalized linear model with a poisson or negative binomial link function. Since our response variable is a count a distribution like the poisson or negative binomial are a better fit.