Description

Assignment Sheet 4
1. The dataset Dates.csv contains 3000 dates from years 2000 to 2015. Read it into SAS.
(b) Write code to screen the dataset.
(c) List any errors identified.
2. The dataset Bricks.csv contains information on Australian quarterly clay brick production from 1956 to 1994. Read the data into SAS.
(b) Create a time series plot of the data and comment (briefly – one to two sentences) on the effects (or not) of season, cycle and trend.
(c) Use an appropriate exponential smoothing method to forecast to the end of 1996. In your answer, state which type of exponential smoothing you used and why, provide a graph illustrating the forecasts, and give a table of the forecasts with confidence limits.
3. The dataset LakeHuron.csv contains annual depth measurements at a specific site on Lake Huron from 1875 to 1972. Read the data into SAS.
(a) Create four new variables that contain the time series depth measurements at lag 1 to 4.
(b) Generate scatterplots of depth versus each lag variable.
(c) Comment on autocorrelation in the data.
Details on what you have to submit for this assignment Submission of this assignment is in two parts:
1. Submit on Moodle the SAS programme (code only, no output) you createdto address Qu’s 1-3 above. This must be done before the start of class, i.e. BEFORE 2PM. Do not leave this until the last minute as Moodle submission will close at this time.
2. Submit a printed hard copy of your programme (code only, no output), a hardcopy of your answer to Qu 1 (c), Qu 2 (b) and (c), and Qu 3 (b) and (c). This will be submitted at the beginning of the lecture at 2pm.