Question 4 For each of the following time series plots, explain what type of transformation, if any, would make the series stationary. (3 marks). 1) 7000- 5000- 4000 - 1960 01 1980 01 2000 01 2) 1960 1980 2000Question 6 The variable yearly income is examined in a regression setting where the predictor variable is lag (1) of income and the following output is produced. i". c) Explain whether this model is appropriate to use for forecasting based on this output. (2 marks).Question 7 The following plots have been obtained for a time series. a) Suggest an appropriate ARIMA model based on the below plots. (2 marks). 120 - 90 - 60 - 30 - 1980 Jan 1990 Jan 2000 Jan 2010 Jan 2020 Jan Month 0.2 - 0.2- 0.1 -- 0.1 -- act pact 0.0 0.0 0.1 - - - 12 18 24 12 18 24 lag [1M] lag [1M]b) The following ARIMA output has been obtained from R. Based on this output, explain which model would you recommend for forecasting? (2 marks). # # . model sigma2 log_lik AIC AICC BIC AY roots Ma roots # # ## 1 arimaOil 17.1 -1393. 2787. 2787. 2806. # # 2 arimal10 17.1 -1399. 2780. 2780. 2815. # # 3 auto 17.4 -1392. 2788. 2788. 2807. c) If your selected model in part b) above has a p-value of 0.900 in the Ljung-Box test, would you recommend using this model? Explain why or why not. (2 marks)