Can someone help me answer and understand? I am trying to teach myself

Answer the following. (a) True or false, and explain. If a regression model has high bias, it is unlikely that collecting more data to train/build the model will increase its performance on a validation or test set (with respect to, say, SSE, MSE, or R2). (b) What do you think will happen to the variance of an over-fitted regression model as the size of the training set increases? (c) Suppose you build polynomial regressions in one variable (y = Bo + ER=1 Bkak), and you want to choose the polynomial degree by evaluating the performance of your models (using say, MSE) on a validation or test set. Once you choose the final model, would you expect the test MSE to be higher or lower than the training MSE? Why? (d) Suppose when you add flexibility to your model by adding higher order terms or more predictor variables that you begin to see the test or validation error (MSE or SSE evaluated on the test or validation set) begin to increase away from the training error. What kind of a problem are your models experiencing