9 10 o 7 2 points Which of the following is not a problem as long as we're aware that it is occurring: multicollinearity heteroscedasticity autocorrelation misspecification 2 points In residual analysis, there is generally not a problem if the residuals are arranged in a linear or curvilinear pattern. True False 8 2 points If we are using least squares regression for time series forecasting and we find that we have autocorrelation, what should w nothing, just be aware that we should not interpret the betas use an autoregressive error model instead transform the x variables transform the y variables 9 2 points You might want to use stepwise regression when: You don't have enough predictors to utilize multipl regression You want to eliminate some of your less useful predictors in a complex model You have a very simple regression model and don't need the full model-building process Vauhallove unu maha vinisting enmo secumntione nfloset ensree ronroccinn