1. What is normal anyway?! Disclaimer: I am not an expert!

2. When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model.
There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions.

3. QQ PLOTS Test for normality
Ranked samples from our distribution plotted against a similar no. of ranked quantiles taken from a ND
1,6,9 good

4. Constancy of variance
A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis.
If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data – uncorrelated

5. It means that when you plot the individual error against the predicted value, the variance of the error predicted value should be constant. See the red arrows in the picture below, the length of the red lines (a proxy of its variance) are the same.

6. Constancy of variance
no heteroscedasticity of residuals=this means that the variance of residuals should not increase with fitted values of response variable.
Residuals -essentially the distance of the data points from the fitted regression line

7. Idealised examples : Residuals V fitted values

8. residuals appear exhibit homogeneity, normality, and independence
residuals appear exhibit homogeneity, normality, and independence. Those are pretty clear, although I’m not sure if the variation in residuals associated with the predictor (independent) variable Month is a problem. This might be a problem with heterogeneity

9. Bad

10. sop<-lmer(log(subnatcov+1)~iapcov+avmoisture+loi+cov+P+ss+channel
sop<-lmer(log(subnatcov+1)~iapcov+avmoisture+loi+cov+P+ss+channel.slope+domnatcov+iapcov:avmoisture+iapcov:cov+(1|river)+(1|trans), data=finalscale, REML=FALSE)

14. Better SQRT transform

15. sop<-lmer(sqrt(subnatcov+1)~iapcov+avmoisture+loi+cov+P+ss+domnatcov+channel.slope+(1|river)+(1|trans), data=finalscale, REML=FALSE)

19. GLMER: Should we check the spread of points for the random effect
GLMER: Should we check the spread of points for the random effect? Homogeneity

20. No transformations Layered abundance data
mod<- lmer(abundance~iapcov+avmoisture+loi+cov+P+ss+domnatcov+slope
+(1|river),
data=finalscale, REML=FALSE)