could you tell me what the correct options for these problems ? Please explain.

Problem 1:

Suppose we have a dataset with stock prices (Y) on different time points (X). After performing a linear regression of Y on x, we get the following plot of residuals vs tted values. Which of the following statements. in your opinion, are true? Select all that apply. Choose as many as you like E Linear regression is not appropriate as residuals are not independent Residuals might not be normally distributed, but they are independent Nonlinear regression with independent errors should be used for this data. E Linear regression gives us good results and there is no need to use any other model. Ordinary least squares method was used to fit a regression model to predict income (in thousands of dollars) from the following predictors: x1 = education X2 = gender (where male = 0 and female = 1) X3 = interaction between education and gender The model produced the following coefficients: 3;, = -11.52.31 = 2.99. 52 = 1.01.3a = -0.94. On average, how much income do men and women get if they have 20 years of education? Men: $48.28 thousand; Women: $28.08 thousand Men: $59.80 thousand; Women: $41.00 thousand E Men: $59.80 thousand; Women: $20.20 thousand A (D '<