Diving further into the analysis of last time of the linear model that we created to fit the hotel rates given we have the available occupancies for the related data. We found a high R squared value and adjust R value which meant it showed a high degree of relation however it does not mean it’s causing it.
We further investigate this phenomenon by looking at what we can do to further analyse the model by looking at the various different plots and what they actually tell us in terms of the erros and outliers that our model is equipped to handle or not since it is a linear model it is not terribly flexible but it does so by not introducing a lot of bias into the model and causing any amount of overfitting or large variances.
Looking at the plots for our data we can tell that there are not any important problems right away that we can tell of.
Our Residuals vs Fitted do show some amount of balloning in terms of the way the data is spread out with increase in value indicating that there may be some heteroskedasticity.
Our QQ plots do not show any skewness and show mostly normalization in behavior which is fine for us for now.
This is just a normalized version of the residuals vs fitted curves and this does not show any better than our first which still shows heteroskedaticity.
From leverage plots we are able to tell if there are any outliers in our data and how they affect the fit in terms of the leverage or pull they provide. It provides that there’s not a lot of outliers pulling a lot of leverage.