Data

Writing

(noun)

pieces of information.

Related Terms

  • method
  • result
Marketing

(noun)

Data are values of qualitative or quantitative variables belonging to a set of items; Data are typically the results of measurements and can be visualised using graphs or images

Related Terms

  • mall intercept
  • scientific method

Examples of Data in the following topics:

  • Data and Information

    • Data consists of nothing but facts, which can be manipulated to make it useful; the analytical process turns the data into information.
    • Binary files (readable by a computer but not a human) are sometimes called "data" and are distinguishable from human-readable data, referred to as "text" .
    • Once data is in digital format, various procedures can be applied on the data to get useful information.
    • Data processing may involve various processes, including:
    • Data processing may or may not be distinguishable from data conversion, which involves changing data into another format, and does not involve any data manipulation.
  • Data Snooping: Testing Hypotheses Once You've Seen the Data

    • Testing hypothesis once you've seen the data may result in inaccurate conclusions.
    • The error is particularly prevalent in data mining and machine learning.
    • Sometimes, people deliberately test hypotheses once they've seen the data.
    • Data snooping (also called data fishing or data dredging) is the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data.
    • Although data-snooping bias can occur in any field that uses data mining, it is of particular concern in finance and medical research, which both heavily use data mining.
  • Analyzing Data

    • Data Analysis is an important step in the Marketing Research process where data is organized, reviewed, verified, and interpreted.
    • Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.
    • In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
    • All are varieties of data analysis.
    • Summarize the characteristics of data preparation and methodology of data analysis
  • MLA: Reporting Data

  • APA: Reporting Data

  • Chicago/Turabian: Reporting Data

  • MLA: Reporting Data

  • Chicago/Turabian: Reporting Data

  • Observations, variables, and data matrices

    • These observations will be referred to as the email50 data set, and they are a random sample from a larger data set that we will see in Section 1.7
    • The data in Table 1.3 represent a data matrix, which is a common way to organize data.
    • Data matrices are a convenient way to record and store data.
    • How might these data be organized in a data matrix?
    • These data were collected from the US Census website.
  • Analyzing Data and Drawing Conclusions

    • In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
    • In an exploratory analysis, no clear hypothesis is stated before analyzing the data, and the data is searched for models that describe the data well.
    • Coding is the process of categorizing qualitative data so that the data becomes quantifiable and thus measurable.
    • How data is coded depends entirely on what the researcher hopes to discover in the data; the same qualitative data can be coded in many different ways, calling attention to different aspects of the data.
    • Coded data is quantifiable.
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