|
|
|
|
| LEADER |
02521nmm a2200409 u 4500 |
| 001 |
EB001918422 |
| 003 |
EBX01000000000000001081324 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 050 |
|
4 |
|a QA76.9.D343
|
| 100 |
1 |
|
|a North, Matthew
|
| 245 |
0 |
0 |
|a Does correlation prove causation in predictive analytics?
|c Matthew North
|
| 260 |
|
|
|a [Place of publication not identified]
|b Infinite Skills
|c 2017
|
| 300 |
|
|
|a 1 streaming video file (4 min., 56 sec.)
|b digital, sound, color
|
| 653 |
|
|
|a Business forecasting / Mathematical models / fast / (OCoLC)fst00842702
|
| 653 |
|
|
|a Data Mining
|
| 653 |
|
|
|a correlation / aat
|
| 653 |
|
|
|a Prévision commerciale / Modèles mathématiques
|
| 653 |
|
|
|a Data mining / fast / (OCoLC)fst00887946
|
| 653 |
|
|
|a Corrélation (Statistique)
|
| 653 |
|
|
|a Business forecasting / Data processing / fast / (OCoLC)fst00842701
|
| 653 |
|
|
|a Business forecasting / Mathematical models
|
| 653 |
|
|
|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
|
| 653 |
|
|
|a Correlation (Statistics) / http://id.loc.gov/authorities/subjects/sh85033032
|
| 653 |
|
|
|a Prévision commerciale / Informatique
|
| 653 |
|
|
|a Business forecasting / Data processing
|
| 653 |
|
|
|a Exploration de données (Informatique)
|
| 653 |
|
|
|a Correlation (Statistics) / fast / (OCoLC)fst00880312
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 500 |
|
|
|a Title from resource description page (Safari, viewed May 15, 2017)
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/9781491990858/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 000
|
| 082 |
0 |
|
|a 519.5
|
| 082 |
0 |
|
|a 330
|
| 520 |
|
|
|a "One of the biggest mistakes made by a business or data analyst is incorrectly interpreting the results of your model; and forming faulty conclusions based on that data. In this video, Matt North shows you how to create a simple correlational matrix in RapidMiner; and gives a specific explanation for the interpretation of the coefficients, including understanding relative strength and statistical significance. It is important to recognize whether or not there is evidence in the data to support a claim of related items, how to defend those conclusions, and understand when to take the investigation further before making a claim based on a single model's results. To get the most out of this video, you will need a basic understanding of statistics, and know how to compare variables in a data set."--Resource description page
|