Project You are a statistical consultant in which a client has come to you with a data set for which they want you to analyze. The data set consists of monthly CO2 levels at Alert, Northwest Territories, Canada. The data was collected from January 1994 through December 2004. You can access the data from the TSA package in the data set called co2. The client wants you to analyze the data and give a report on your analysis. Their ultimate goal is to forecast the CO2 levels for every month of 2005. They would also like some type of interval forecasts. Type up the analysis for the client (Be neat and organized). Be sure you give reasons for any and all analysis that you do. Also, show your results and interpret them for your client. Show how you obtained your results (that is, include your R code). Grades will be given based on: Organization or the report. Correct analysis of the data. Justifying each part of your analysis. Thoroughly explaining all results for the client.1. Plot the observed data. Describe any pre-analysis steps needed (removing outliers and/or transforming the data) which you may see from looking at the data. If you do any of these pre-analysis steps, then plot the series again after these steps are taken. 2. Remove any trend and/or seasonal components of the data. Describe the methods you use to remove these components and provide plots of each component and plot the residuals. 3. After the deterministic components have been removed, plot the ACF and PACF for the residuals. From these plots, explain what times series models we should consider. 4. Find the best model using an AIC criteria. Give the expression for the best fitted model. Plot the residuals after the model is fitted 5. After plot the residuals check to see if there is any significant dependence structure left. This can be done with the ACF and PACF plots or with some test such as Ljung and Box 6. Using the deterministic components and time series model in the previous parts, forecast the the CO2 levels for every month of 2005. Give the value of the forecasts and make an inference on them.