exploratory data analysis

Examples of exploratory data analysis in the following topics:

• Exploratory Data Analysis (EDA)

  • Exploratory data analysis is an approach to analyzing data sets in order to summarize their main characteristics, often with visual methods.
  • Exploratory data analysis (EDA) is an approach to analyzing data sets in order to summarize their main characteristics, often with visual methods.
  • Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data and possibly formulate hypotheses that could lead to new data collection and experiments.
  • Exploratory data analysis, robust statistics, and nonparametric statistics facilitated statisticians' work on scientific and engineering problems.
  • Tukey held that too much emphasis in statistics was placed on statistical hypothesis testing (confirmatory data analysis) and more emphasis needed to be placed on using data to suggest hypotheses to test.

• References

  • An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252.

• Statistical Graphics

  • Statistical graphics are used to visualize quantitative data.
  • Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form.
  • Exploratory data analysis (EDA) relies heavily on such techniques.
  • If one is not using statistical graphics, then one is forfeiting insight into one or more aspects of the underlying structure of the data.
  • Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data.

• Analyzing Data

  • Data Analysis is an important step in the Marketing Research process where data is organized, reviewed, verified, and interpreted.
  • 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.
  • Types of data analysis outputs: heat map, bar plots, scatter plots.
  • Summarize the characteristics of data preparation and methodology of data analysis

• 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 the main analysis phase, either an exploratory or confirmatory approach can be adopted.
  • 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.
  • The type of data analysis employed can vary.
  • Sociological data analysis is designed to produce patterns.

• Stem-and-Leaf Displays

  • This graphical technique evolved from Arthur Bowley's work in the early 1900s, and it is a useful tool in exploratory data analysis.
  • Stem-and-leaf displays became more commonly used in the 1980s after the publication of John Tukey 's book on exploratory data analysis in 1977.
  • Consider the following set of data values:
  • The display for our data would be as follows:
  • However, stem-and-leaf displays are only useful for moderately sized data sets (around 15 to 150 data points).

• Descriptive or Inferential Statistics?

  • Descriptive statistics are distinguished from inferential statistics in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
  • Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.
  • These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
  • More recently, a collection of summary techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot .
  • In the business world, descriptive statistics provide a useful summary of security returns when researchers perform empirical and analytical analysis, as they give a historical account of return behavior.

• Basic Descriptive Statistics

  • Statistics deals with the collection, analysis, interpretation, and presentation of numerical data.
  • The mean is the average value of a data set.
  • Thus the median of this data set is 3.5.
  • A data set involves a range of values.
  • This is better known as "exploratory data analysis".

• Defining Objectives and Formulating Problems

  • An example of problem definition is reviewing secondary data about a recently launched product and identifying that there seem to be more unmet needs that should be further explored to enhance advertising communication and better connect with the target consumer.
  • Marketing research uses the scientific method in that data are collected and analyzed to test prior notions or hypotheses.
  • This stage involves discussion with the decision makers, interviews with industry experts, analysis of secondary data, and, perhaps, some qualitative research, such as focus groups.

• Plan the Research Design

  • Conducting exploratory research, precisely defining the variables, and designing appropriate scales to measure them are a part of the research design.
  • This process is guided by discussions with management and industry experts , case studies and simulations, analysis of secondary data, qualitative research, and pragmatic considerations.
  • Decisions are also made regarding what data should be obtained from the respondents (e,g,, by conducting a survey or an experiment).
  • The research plan outlines sources of existing data and spells out the specific research approaches, contact methods, sampling plans, and instruments that researchers will use to gather data.
  • Secondary data analysis is one of the steps involved in formulating a Research Design