Chapter 3: Normalizing the Data –

1. Chapter 3: Normalizing the Data – Adding It All Up Equated Day Factors Seasonally-Adjusted Data: Initial Initial Seasonal Factors Original Data Normalized Data Holiday Factors Growth Rate (Adjustments) Seasonally-Adjusted Data: Initial Seasonally-Adjusted Data: Final Growth-Adj Seasonal Factors Events (Adjustments) 1 3 -

2. Introduction Net Daily Factors Normalization Factors Normalized Data Normalizing monthly data refers to the process of adjusting each month’s data so that every month is of equivalent length. Normalizing the Data: Adding It All Up Month Lengths, 2016 Average 2 1. Introduction 3 -

3. Introduction Net Daily Factors Normalization Factors Normalized Data How do we normalize the data? Multiply the EDFs by the Holiday Factors (all non-holidays have a “factor” of 1.00) to derive Net Daily Factors. Add up the Net Daily Factors for each month to arrive at each month’s length. Divide each month’s length by the average month’s length to arrive at a Normalization Factor for each month. Divide each month’s data by its Normalization Factor to express it as “Normalized Data”, where every month is of equal length, and now ready to be seasonalized. Normalizing the Data: Adding It All Up 3 3 -

4. Introduction Net Daily Factors Normalization Factors Normalized Data In order to calculate the Net Daily Factors, we need to bring in the developed Equated Day Factors (EDFs) & Holiday Factors. Normalizing the Data: Adding It All Up Normalizing Data Template: Inputs 4 2. Net Daily Factors 3 -

5. Introduction Net Daily Factors Normalization Factors Normalized Data Formulas in a “Calc” tab pick up the EDFs & Holiday Factors for the entire covered period. Normalizing the Data: Adding It All Up Normalizing Data Template: Calc Net Month Length (Jan 2016): 19.45 5 3 -

6. Introduction Net Daily Factors Normalization Factors Normalized Data Net Daily Factors are calculated by simply multiplying each day’s EDF by it’s Holiday Factor; summing them arrives at the “true” Net Month Length. Normalizing the Data: Adding It All Up Normalizing Data Template: Calc Net Month Length (Jan 2016): 19.45 6 3 -

7. Introduction Net Daily Factors Normalization Factors Normalized Data Monthly and annual totals are calculated for the entire period, along with overall averages. Normalizing the Data: Adding It All Up Totals by Month Totals by Year Overall Totals Normalizing Data Template: Output 7 3 -

8. Introduction Net Daily Factors Normalization Factors Normalized Data Monthly data can be put into a table to more easily observe how month lengths vary over time. Normalizing the Data: Adding It All Up (Note: table does not capture 3rd & 4th Friday factors.) Normalizing Data Template: Output 8 3 -

9. Introduction Net Daily Factors Normalization Factors Normalized Data Year lengths vary slightly, and leap years are not necessarily the longest. Normalizing the Data: Adding It All Up Normalizing Data Template: Output 9 3 -

10. Introduction Net Daily Factors Normalization Factors Normalized Data Month over month lengths can change significantly. Normalizing the Data: Adding It All Up Normalizing Data Template: Output 10 3 -

11. Introduction Net Daily Factors Normalization Factors Normalized Data Month lengths can also change dramatically year-over-year. Normalizing the Data: Adding It All Up Normalizing Data Template: Output 11 3 -

12. Introduction Net Daily Factors Normalization Factors Normalized Data Almost half the time, year-over-year month lengths change by 5% or more; more than 10% of the time, they change by 10% or more. Normalizing the Data: Adding It All Up Normalizing Data Template: Output 12 3 -

13. Introduction Net Daily Factors Normalization Factors Normalized Data Normalization Factors compare each month’s Net Length with the Average Net Month Length. Normalization Net Month Length Factor Average Month Length Normalizing the Data: Adding It All Up = Example: Jan 2016 19.45 Days / 20.67 Days = 0.94 = 13 3. Normalization Factors 3 -

14. Introduction Net Daily Factors Normalization Factors Normalized Data Normalization Factors are calculated for every month for the entire period. Normalizing the Data: Adding It All Up Totals by Month Totals by Year Overall Averages Normalizing Data Template: Output 14 3 -

15. Introduction Net Daily Factors Normalization Factors Normalized Data Normalizing the data requires dividing each month’s Actual amount by its Normalization Factor. Normalized Actual Data Data Normalization Factor Normalizing the Data: Adding It All Up = Example: Jan 2016 29.393 Billion / 0.94 = 31.236 Billion = 15 4. Normalized Data 3 -

16. Introduction Net Daily Factors Normalization Factors Normalized Data Original Actuals are normalized for every month for the entire period. Normalizing the Data: Adding It All Up Totals by Month Totals by Year Overall Annual Averages Normalizing Data Template: Output 16 3 -

17. Introduction Net Daily Factors Normalization Factors Normalized Data So what impact is made on the Original Actuals when the data is normalized? Here are the Actuals. Normalizing the Data: Adding It All Up Actuals 17 3 -

18. Introduction Net Daily Factors Normalization Factors Normalized Data While not always the case, normalizing the data usually helps smooth out some of the volatility in the data. Normalizing the Data: Adding It All Up Actuals Normalized 18 3 -

19. Introduction Net Daily Factors Normalization Factors Normalized Data An aside on Retail Trade: Many in this industry like to divide the “months” into weeks of 4-4-5. There are some issues with this approach. Some 4-week months may not capture the 1st day and/or last day of the calendar month. Decembers are obviously especially crucial, but when they can “end” several days before or after New Year’s Day, year-to-year comparisons can be compromised. Fails to capture the significance of what day of the week Christmas falls. Some holidays may be uncooperative with this approach. (e.g., Memorial Day falling in May or June). Every 5-6 years has an extra week that may be problematic. Normalizing the Data: Adding It All Up 19 3 -