READ Chapter 2 attached on pages 87-91
ANSWER to chapter 2's Exercise questions 3, 8, 9, 10, 11, and post at least 2 points that he/she has learned from them and at least 2 questions that he/she may have. ( Must be at least 100 words for this last portion)
|Chapter 2: The Forecast Process, Data Considerations, and Model Selection|
|1. Use ACF to identify the data pattern:|
|a. A stationary series: the value of ACF diminishes rapidly (drops after|
|the second or third time lag) toward zero as k increases|
|b. A series with the trend (a non-stationary series): the value of ACF|
|declines toward zero slowly|
|c. A series with seasonality: ACF (4, 8, …) is significant for quarterly|
|data and ACF (12, 24, …..) is significant for monthly data|
|d. A random series: ACF for all lags are not significantly different from|
|2. Homework: Exercises 3, 8, 9, 10, and 11 (Please use Forecast X for this exercise)|
|8b. To obtain autocorrelation using Forecast X:|
|First, highlight "Year" and "Larceny Thefts" data > click "Add-Ins" at the top > click Forecast X > chick "Data Capture" inside the Forecast X dialog box|
|Make sure "Data is Organized In" "Columns"|
|Inside "Data Set" check "Contain Dates" > select "Annual" for Periodicity and "1" for Labels|
|Click "Forecast Method" next to "Data Capture" > click "Analyze" and you will have a 12-period plot of autocorrelation function (ACF)|
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