Bass Model using R

  In this assignment you will fit the Bass model using the Car Navigation Systems (CNS) sales dataset using R. Download the spreadsheet containing the data for the Car Navigation Systems (CNS) sales heren. Most of the assignment is a redo of previous assignment but using R. Modify my R code to so that you can run the Sony CNS data set thru my R Code. Cut and past the following key results obtained from R into a document: 1. Use regression to estimate the Bass model. Show the regression results. 2. From the regression parameters calculate the values of p, q, p+q and M for the Bass model. 3. Calculate the absolute value of the residual % [(actual-fitted values) / actual] of the model? Graph the absolute value %. 4. Forecast sales beyond the year 2000. Show the forecasts. 5. Deconstruct estimated and forecasted sales into sales from innovators and sales from imitators. Generate a graph with year on the X axis and estimated sales from innovators and estimated and forecasted sales from imitators on the Y axis. 6. Calculate the timing of peak adoption, and the peak sales using the formulae given for the bass model. Calculate the 95% intervals for the timing of peak adoption and peak sales. 7. Provide some suggestions to improve my code. The suggestion can be a redo of a graph with more elegant graphing, graphs that are cool eye candy and / or some statistical analysis, function or routines that are an extension of the analysis that you find interesting. You can dig deeper into the LM package if that interests you. Deliverables: 1. Neatly formatted write-up to the questions above. The write-up should be cleanly formatted with appropriate tables and figures placed in the text, with names clearly visible at top of document. Hard copy due in class. 2. A copy of your R Code named as a file with the extension .r. Upload 3. The csv input data file that you refer to in the R code. Please make sure that the name of this data file matches what is in your R code, otherwise my test run of your R code will fail. Upload.