Solve it by R languages and give me the output also

data :

https://drive.google.com/file/d/1ZBNAt9rxG6eWoGJRp86r2ONjPbfa1I8e/view?usp=drivesdk

The attached data is for the weekly mortality rate of heart disease (mortality rate means deaths per 1,000 individuals per week) in California between 1980-1989. Download the data in to R and answer the following questions: 1. Plot the time series and check for trend or seasonal effect. Comment on the plot. 2. Plot the ACF and PACF. Comment on the two plots. 3. Is the time series stationary? comment on the stationarity based on ADF test. 4. Find the sample autocorrelation values rk and sample partial autocorrelation values rkk, for k=0,1,2,3,4,5,6,7,8,9,10. 5. Specify the suitable initial model for the series based on rk and rkk from the previous question. (State the hypothesis, the test statistics and the conclusion) 6. Fit the model AR(1), check the significance of the estimates and check the residuals. Then comment on the model adequacy. 7. Fit the model AR(2), check the significance of the estimates and check the residuals. Then comment on the model adequacy. 8. Fit the model ARMA (1,1), check the significance of the estimates and check the residuals. Then comment on the model adequacy. 9. Write down the mathematical formula to calculate the estimate of constant () in all AR(1),AR(2), and ARMA(1,1) model of the pervious questions. 10. Compare the criterions, which model do you think it's appropriate model based on the previous analysis? Why you choose this model? 11. Write down the mathematical formula of the final model you decide with its estimated parameters. 12. Write down the mathematical formula of the final model in question 11 using the backshift operator. 13. Is the model you choose stationary, invertible? Justify your answer? 14. Find the first three and weights of your model in question 11. 15. Compute the first three ahead forecast with their CI