Statistics for business decision making and analysis robert stine and foster chapter 02

Chapter Data Copyright © 2011 Pearson Education, Inc 2.1 Data Tables Some Basic Ideas  Data are a collection of numbers, labels, or symbols with context  A data table is a rectangular arrangement of data with rows and columns  Observations or cases form the rows; common attributes or variables form the columns of 25 Copyright © 2011 Pearson Education, Inc 2.1 Data Tables Disorganized Data of 25 Copyright © 2011 Pearson Education, Inc 2.1 Data Tables Same Data in a Data Table of 25 Copyright © 2011 Pearson Education, Inc 2.1 Data Tables  Organize data to yield meaningful information  Provide context (e.g., who, what, when)  Improve interpretability with meaningful names, formatting and units of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and Numerical Data Categorical Data  Also called qualitative or nominal variables  Identify group membership  Type of purchase made and Brand of bike are examples of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and Numerical Data Numerical Data  Also called quantitative or continuous variables  Describe numerical properties of cases  Have measurement units  Size of bike (cm) and Amount spent ($) are examples of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and Numerical Data Measurement Scales     Nominal – name categories without implying order (categorical) Ordinal – name categories that can be ordered (categorical) Interval – numerical values that can be added or subtracted (no absolute zero) Ratio – numerical values that can be added, subtracted, multiplied or divided (makes ratio comparisons possible) of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and Numerical Data Likert Scale (Ordinal – to Categories) 10 of 25 Copyright © 2011 Pearson Education, Inc 2.3 Recoding and Aggregation  Recode: building a new variable from another (recoding price into expensive or inexpensive)  Aggregate: reduce rows in a data table by counting or summing values within categories 11 of 25 Copyright © 2011 Pearson Education, Inc 2.3 Recoding and Aggregation An Example of Aggregation 12 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.1: MEDICAL ADVICE Motivation Are patients from one HMO more likely to visit the doctor than those from another HMO? 13 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.1: MEDICAL ADVICE Method Gather data and organize in a data table Cases that make up the rows are office visits The following variables make up three columns: Patient ID; HMO Plan; and Duration of patient’s office visit 14 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.1: MEDICAL ADVICE Mechanics 15 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.1: MEDICAL ADVICE Message Aggregate the duration of office visits to learn whether patients from one plan are consuming most of the doctor’s office time 16 of 25 Copyright © 2011 Pearson Education, Inc 2.4 Time Series Some Definitions  Time series – data recorded over time  Timeplot – graph of a time series showing values in chronological order  Frequency – regular time spacing of data in a time series (e.g., daily, monthly, etc.)  Cross-sectional – data observed at the same time 17 of 25 Copyright © 2011 Pearson Education, Inc 2.4 Time Series Timeplot of Monthly Unemployment Rate 18 of 25 Copyright © 2011 Pearson Education, Inc 2.5 Further Attributes of Data Useful to Know  When and where the data were collected  Source of the data (available online?)  How the data were collected 19 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.2: CUSTOMER FOCUS Motivation How customers in a focus group react to a new product design? 20 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.2: CUSTOMER FOCUS Method Gather data and organize in a data table The cases that make up the rows are participants in the focus group One of the variables that make up the columns is participants’ ratings of the product 21 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.2: CUSTOMER FOCUS Mechanics In addition to product ratings, the columns should include characteristics of the participants such as name, age (in years), sex, and income 22 of 25 Copyright © 2011 Pearson Education, Inc 4M Example 2.2: CUSTOMER FOCUS Message Determine who likes the design (younger or more affluent members of the focus group, for example) and choose advertising that appeals to this group 23 of 25 Copyright © 2011 Pearson Education, Inc Best Practices  Provide a context for your data  Use clear names for your variables  Distinguish numerical data from categorical data  Track down the details when you get the data  Keep track of the source of data 24 of 25 Copyright © 2011 Pearson Education, Inc Pitfalls  Do not assume that a list of numbers provides numerical data  Don’t trust all of the data that you get from the Internet  Don’t believe every claim based on survey data 25 of 25 Copyright © 2011 Pearson Education, Inc  ... information  Provide context (e.g., who, what, when)  Improve interpretability with meaningful names, formatting and units of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and. .. data table is a rectangular arrangement of data with rows and columns  Observations or cases form the rows; common attributes or variables form the columns of 25 Copyright © 2011 Pearson Education,... Identify group membership  Type of purchase made and Brand of bike are examples of 25 Copyright © 2011 Pearson Education, Inc 2.2 Categorical and Numerical Data Numerical Data  Also called quantitative