1. Chapter 10 – Basic Regression Analysis with Time Series Data

2. What is Time Series Data and why is it Different? There is a time ordering of the data The past can affect the future, but the future cannot affect the past. Example: National population from 1900 to 2006 (data set NATPOP)

3. What is Time Series Data and why is it Different? Random nature of times series data Formally, the process that generates time series data is called a stochastic or time series process

4. What is Time Series Data and why is it Different? Random nature of times series data Random sample from a population vs. random sample of time series data

5. Examples of Time Series Data: Uncorrelated data, constant process model

6. Examples of Time Series Data: Autocorrelated data

7. Examples of Time Series Data: Trend

8. Examples of Time Series Data: Cyclic or seasonal data

9. Examples of Time Series Data: Nonstationary data

10. Examples of Time Series Data: A mixture of patterns

11. Cyclic patterns of different magnitudes

12. Atypical events

13. 13 Atypical events

14. Famous Time Series Expert – Yogi Berra The future ain’t what it used to be.

15. Famous Time Series Expert – Yogi Berra You can observe a lot just by watching. The basic graphical display for time series data is the time series plot which is just a graph of the observations vs. time periods.

16. Time Series Plot Example Open TRAFFIC2 data set and make time series plot of year vs. statewide total accidents (totacc) In Minitab need to choose series and time stamp

17. Time Series Plot Example

18. Time series plots Notice that the histograms look very similar even though the time series behavior is very different

19. Histogram of totacc

20. When there are two or more variables of interest, scatter plots can be useful

21. Forecasting It is difficult to make predictions, especially about the future. – Neils Bohr

22. Forecasting

23. Forecasting is useful in many fields: Business and industry Economics Finance Environmental sciences Social sciences Political sciences

24. Data Analysis Process: 1.Problem definition 2.Data collection 3.Data analysis 4.Model selection and fitting 5.Model validation 6.Model deployment 7.Monitoring forecasting model performance

25. Time Series Example – Data Set FERTIL3 gfr – number of children born to every 1,000 women of childbearing age from 1913 to 1984. Make a time series plot of gfr

26. Time Series Example – Data Set FERTIL3

27. pe – average real dollar value of the personal tax exemption from 1913 to 1984. Make a time series plot of pe

28. Time Series Example – Data Set FERTIL3

37. Scatter plot of gfr vs. pe

38. Time Series Example – Data Set FERTIL3 What could affect general fertility rate in the U.S.? Many things! How about these two: World War II Availability of the birth control pill

39. Time Series Example – Data Set FERTIL3 ww2 is a dummy variable 1 if year is 1941 through 1945 0 otherwise pill is a dummy variable 1 if year is 1963 or greater 0 otherwise

40. Time Series Example – Data Set FERTIL3

49. gfr = 98.7 + 0.0825 pe - 24.2 ww2 - 31.6 pill ww2 is a dummy variable 1 if year is 1941 through 1945 0 otherwise

50. gfr = 98.7 + 0.0825 pe - 24.2 ww2 - 31.6 pill pill is a dummy variable 1 if year is 1963 or greater 0 otherwise

51. Time Series Example – Data Set FERTIL3

54. Making lags in Minitab is easy. Go to Stat > Time Series > Lag.

55. Time Series Example – Data Set FERTIL3