Can someone help me with the R code needed to complete this?

The Blaisdell Company wanted to use industry sales to predict its sales. Adjusted quarterly sales data for 1998 - 2002 are in the following data. The second column of data are observations of Blaisdell's sales, and the last column contain industry sales. The very first column, the one with the row numbers, is a time index: 1 means first quarter of 1998, 2 means 2nd quarter of 1998, etc. Data: 1 20.96 127.3 2 21.96 130 3 22.39 132.7 4 23.48 129.4 1 24.10 135 2 24.54 137.1 3 25.00 141.2 4 26.36 141.8 27.52 145.5 W N H 28.24 145.3 21.40 148.3 4 21.52 146.4 22.76 150.2 23.66 153.1 3 24.01 157.3 4 24.30 160.7 1 25.64 164.2 2 26.98 165.6 3 27.78 168.7 4 28.78 171.7 (b) Make a scatter plot of company sales versus the time index. You might have to create a new column for the time values or figure out how to reference the row numbers in R. (c) Use R to run a Neumann-Durbin-Watson to check for autocorrelation of company sales over time: Ho : p = 0; Ha : p does not = 0: (d) How would you suggest to proceed in modeling the relationship between these variables