1.use the q10 dataframe. consider three standard multiple linear regression models predicting sales from both predictors and no interaction (i. e., ignoring the time series nature of the data):
one with a linear trend, one with a quadratic trend, and one with a cubic trend. in the model with lowest of the three aics, ( just write the code for rstudio)

2.reconsider your model and data from q10. now impose an autoregressive model on the residuals. in other words, consider three additional models predicting sales from quarter and month (no interactions but with whichever kind of trend was appropriate in q10) with an autoregressive model for the residuals of lag 1, lag 2, and lag 3.note: quarter is a categorical variable, independent of the trend, so do not use poly() on it.
in the model with lowest of the four aics, report the standard error of the difference in average sales between first quarter and third quarter. note: this requires installation and loading of the library nlme. so you'll have to do install. packages('nlme') then library(nlme) (just write the code for rstudio)
dataframe 10 is in the regclass of rstudio 's packages