1Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.Chapter 2 Data “In God We Trust.... all others bring data” - UnknownChapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.2A “Simple” Survey Gender Number of siblings Number of countries ever visited Height Favorite coffee flavor at Starbucks Shoe sizeHow could there be “problems” with responses to this survey?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.3Class Activity In groups of 2 (or 3), discuss possible “problems” one might have with getting accurate answers to the questions outlined on the previous page. Be prepared to discuss what you and your teammate discussed with the class.Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.4Real Data Can Be “Messy” Different units of measure. Ambiguous questions lead to ambiguous data. Non-response: how will we handle? Seemingly “ridiculous” responses: how will we handle? An important first step in getting meaningful data is carefully planning the data collection activity AND clearly defining the questions.Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.5What Are Data? Data can be numbers, record names, or other labels. Not all data represented by numbers are numerical data (e.g., 1=male, 2=female). Data are useless without their context…Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.6Raw data What does this data tell us? What does it mean?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.7Data in a context When the data are organized, given labels, and put into a context, the data gain meaning.Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.8The “W’s” To provide context we need the W’s Who What (and in what units) When Where Why (if possible) and Howof the data. Note: the answers to “who” and “what” are essential.Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.92 Types of Variables A categorical (or qualitative) variable is one whose possible values are given by short descriptors Sex (male/female), eye color (blue/brown), favorite food, birth state, flight status, zip codes Can encode as numbers (e.g. female = 1, male = 2) A special kind of categorical variable is ordinaldata (e.g. data where the order is perceived to have worth, but no units, like “Rate your professor on a scale of 1 to 5”, or ranks in the military)Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.102 Types of Variables (Cont.) A quantitative variable is one whose possible values must be numbers, usually with units Income ($), height (inches), weight (pounds) Ratio of left thumb length to right thumb length (unitless) Common business analytics quantitative variables are profit, net increase, tax rate, days in inventory, priceChapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.11Counts• Statisticians often count things• Counts can be categorical or quantitative:• Categorical• Natural summary of categorical data is the count of each typeChapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.12Counts (Cont.) Quantitative Number of CDs in a collection Number of classes taken this semester Number of items returned to seller Number of …Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.132 Types of Variables (Cont.) Why do we differentiate between the two types?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.14Identifying Identifiers Identifier variables are categorical variables with exactly one individual in each category and can look like quantitative variable. Examples: Social Security Number, ISBN, FedEx Tracking Number Should these variables be summarized graphically or numerically? What is the purpose of identifier variables?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.15Class Activity Answer the questions on the next two pages on your own. After you have finished, compare your answers with your teammate(s). If you have any differences in your answers, discuss why you answered the way you did, and try to come to a common answer. Be prepared to discuss any differences you had with the class.Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.16Types of Data Exercises What type of data are these variables?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.17Types of Data Exercises Gender Number of siblings Number of countries ever visited Height Favorite Starbucks flavor Shoe size What type of data are these variables?Chapter02 Presentation 1213Copyright © 2009 Pearson Education, Inc.18Chapter Exampletransaction numbertype of gas# of gallonspay at pump?inside food sale?type of paymentGas purchase in dollarsDay of week9853 premium 22 y n Visa 85.58 Mon9211 diesel 26 y n Am Exp 110.5 Tues8875 regular 19 y y Visa 70.11 Tues8824 regular 21 y y Visa 77.49 Fri8313 regular 14 y y MasterCard 51.66 Wed7699 premium 22 n n cash 85.8 Wed7645 diesel 45 y y Am Exp 191.25 Sat3145 diesel 38 y n Am Exp 161.5 Sat2588 regular 17 n y Visa 62.73 Sun2499 regular 22 n n cash 81.18 Sat2325 premium 15 y n MasterCard 58.35 Fri2291 diesel 22 y n MasterCard 92.4 Mon2078 regular 14 y y Visa 51.66 Thur1843 regular 35 y n Visa 129.15 Thur2103 regular 25 n y cash 92.25
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