= Solution
Check that the chosen mean structure adequately describes the transformed response. Examine the <residual-versus-fitted plot> and residuals versus each predictor: systematic curvature suggests omitted nonlinear effects or an <interaction term>. Plot residual spread against fitted values, for example using a <scale-location plot>, to assess <homoscedasticity>. Compare standardized residuals with a <normal distribution> using a <quantile-quantile plot>, especially for finite-sample <Student's t-distribution> and <F-test> inference.
Also check <independence> using the sampling design and residuals versus time, collection site, or other groups; spatially related photovoltaic systems may have correlated errors that are invisible in a residual-versus-fitted plot. Investigate large <standardized regression residuals>, high <regression leverage>, and influential observations using <Cook's distance>. Check that the <design matrix> has full rank and that severe <multicollinearity> is not making estimates unstable. Reassess whether the response transformation and retained predictors improve these diagnostics and prediction; a coefficient <p-value> alone cannot establish model adequacy. If observations are independent only conditional on site effects, use an appropriate dependence model rather than treating correlated systems as independent replicates.
Back to article page
