6. Using the code below, get a series (it gets a series randomly by using sample() function): set.seed(12345678) myseries % filter(`Series ID` == sample(aus_retail$`Series ID`,1))

6. Using the code below, get a series (it gets a series randomly by using sample() function): set.seed(12345678) myseries <- aus_retail %>% filter(`Series ID` == sample(aus_retail$`Series ID`,1)) see head of your series to check it is a tsibble data, and remove NAs if there is any with these commands: head(myseries) myseries = myseries %>% filter(!is.na(`Series ID`))

a. What is the name of the series you randomly choose? Write it. Run a linear regression of Turnover on trend.(Hint: use TSLM() and trend() functions) See the regression result by report() command. b. By using this model, forecast it for the next 3 years. What are the values of the next 3 years, monthly values? c. Plot the forecast values along with the original data. d. Get the residuals from the model. And check the residuals to check whether or not it satisfies the requirements for white noise error terms.(hint: augment() and gg_tsresiduals() functions)