Chapter 1 notes
Chapter 1 notes BUAL 2600 (Wang)
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This 6 page Class Notes was uploaded by Damian on Wednesday March 9, 2016. The Class Notes belongs to BUAL 2600 (Wang) at Auburn University taught by Yichaun Wang in Spring 2016. Since its upload, it has received 31 views. For similar materials see Business Analytics in Business at Auburn University.
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Date Created: 03/09/16
2016/01/21 Lecture Note (Chapter 1.21.3) 1. The first taxonomy of variable types Categorical (qualitative) variable: When a variable names _________ and answers questions about ______________________________ ______________________________________ ______________________________________ ______________________________________ Ex: Quantitative variable: When a variable has measured ________________________ and the variable tells us about ______________________________. ______________________________________ ______________________________________ ______________________________________ ______________________________________ Ex: Identifier variable: ___________________________________________________________ do not have _______. are a special kind of ____________. are useful in combing data from different sources to avoid _________. it cannot be _________. Ex: 2. The second taxonomy of variable types Nominal variable: ____________________________________________________________ Ordinal variable: ____________________________________________________________ Interval variable: ____________________________________________________________ 1 | P a g e 2016/01/21 Ex: U.S. Census Gender:___________________ Marital status:______________ Education: ________________ Household income:_________ Economic status:___________; Employment status:_______________ Ex: NFL Player profiles and stats Name:______________; Position:_________________; Team:___________________; Hometown:___________; Team standings: _____________; Height:___________; Weight:____________; Season:____________; Passing ATT:____________; Passing YDS:_______________; Passing CMP%:___________; LNG:________________ 3. How to decide your variable types? It depends on __________________________________________________________________ 4. Data source: When: 2 | P a g e 2016/01/21 ____________________________________________________________________________ How: _____________________________________________________________________________ Where: _____________________________________________________________________________ 5. Headsup for data collection Don’t label a variable as categorical or quantitative without thinking about the data and what they represent. Don’t assume that a variable is quantitative just because its values are numbers. Select an appropriate sampling design method for your data collection 3 | P a g e 2016/1/19 Lecture Note (Chapter 1.1) 1. What is business analytics _____________________________________________________________________________ 2. Data warehouses ______________________________________________________________________________ 3. Why companies collect data from you? ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ 4. What are data? Definition: ___________________________________________________________________________ 5. Big data ______________________________________________________________________________ 6. What types of data in data warehouses? Structured data:______________________________________________________________ Semistructured data:_________________________________________________________ Unstructured data:____________________________________________________________ 7. Transactional data Definition:__________________________________________________________________ What techniques are used to analyze transactional data to make decisions or predictions: ______________________________ 8. The features of data All data have a ______, and can be organized into a __________. The rows of a data table correspond to ____________. How many cases presented in this dataset?_________. Respondents:_________________; Subjects or Participants:_____________________; experimental units:____________________________ Variable:___________________________________________________________________ What variables are contained in the first case? __________, ________, ________, _______, _________, ________, _______, _______, ______ 9. Metadata (data about data): ______________________________________________________________________________ ______________________________________________________________________________ 1 | P a g e 2016/1/19 10. Relational database: Ex1: A typical relational database is provided consisting of three relations: customer data, item data, and transaction data. Ex2: Amada, a marketing manager at a credit card bank, wants to know if an offer mailed 3 months ago has affected customers’ use of their cards. To answer that, she asks the information technology department to assemble the information for each customer. What information does Amada need for answering the question? ______________________________ ______________________________ ______________________________ ______________________________ ______________________________ ______________________________ ______________________________ Identify the cases and the variables? 2 | P a g e 2016/1/19 3 | P a g e
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