Multiple regression methods generally depend on the following four assumptions: the residuals of the model are nearly normal, the variability of the residuals is nearly constant, the residuals are in...Multiple regression methods generally depend on the following four assumptions: the residuals of the model are nearly normal, the variability of the residuals is nearly constant, the residuals are independent, and each variable is linearly related to the outcome.
Multiple regression methods generally depend on the following four assumptions: the residuals of the model are nearly normal, the variability of the residuals is nearly constant, the residuals are in...Multiple regression methods generally depend on the following four assumptions: the residuals of the model are nearly normal, the variability of the residuals is nearly constant, the residuals are independent, and each variable is linearly related to the outcome.
In this plot, the ordered residual (or observed quantiles) of the residuals are plotted aginst the expected quantiles assuming that \(\epsilon_i\)'s are approximately normal and independent with mean ...In this plot, the ordered residual (or observed quantiles) of the residuals are plotted aginst the expected quantiles assuming that \(\epsilon_i\)'s are approximately normal and independent with mean 0 and variance = MSE. Heteroscedasticity or unequal variance: the variance of the error \(\epsilon\)i may sometimes depend on the value of Xi. This is often true for financial data, where the volume of transactions usually has a role in the uncertainty of the market.
