|
RAR-files |
| lynda.business.analytics.forecasting.with.trended.baseline.smoothing-apollo.rar |
50,000,000 |
7B0B1735 |
| lynda.business.analytics.forecasting.with.trended.baseline.smoothing-apollo.r00 |
50,000,000 |
BE00D11B |
| lynda.business.analytics.forecasting.with.trended.baseline.smoothing-apollo.r01 |
17,213,805 |
7A9A258E |
|
| Total size: |
117,213,805 |
|
|
| Archived
files |
|
01. Why trended baseline smoothing will help your regression - 56s.mp4 |
5,248,056 |
921CCD1C |
|
02. Software setup - 190s.mp4 |
8,501,952 |
A5980CEE |
|
03. A review of SES with a stationary baseline - 299s.mp4 |
14,017,538 |
4E840984 |
|
04. Problems using SES with a trended baseline - 207s.mp4 |
8,700,674 |
9A474AD8 |
|
05. Forecasting differences - 294s.mp4 |
11,866,370 |
7E4EE24C |
|
06. Using R for SES - 299s.mp4 |
15,236,097 |
55E4EEED |
|
07. Using ARIMA(0,1,1) for SES - 250s.mp4 |
8,029,014 |
135AA973 |
|
08. Distinguish between a level component and a trend component - 208s.mp4 |
6,023,476 |
A150EDAE |
|
09. The trend constant compared to the level constant - 205s.mp4 |
4,999,510 |
3DE6EA49 |
|
10. Compare smoothing and error correction forms - 390s.mp4 |
12,256,262 |
DAF717E9 |
|
11. Initialize the trend forecasts - 302s.mp4 |
10,419,718 |
7716823D |
|
12. Build the full worksheet and optimize with Solver - 410s.mp4 |
17,899,753 |
ECE29439 |
|
13. Prepare for analysis with R - 290s.mp4 |
9,392,300 |
38299067 |
|
14. Run and interpret the analysis in R - 235s.mp4 |
9,826,563 |
4EC993B0 |
|
15. Next steps - 55s.mp4 |
3,434,242 |
EB53C430 |
|
Ex_Files_Analytics_Trend_Forecasting.zip |
763,101 |
A4DF58A6 |
|
| Total size: |
146,614,626 |
|
|
