Examples of exploratory data analysis in the following topics:
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- 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.
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- An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252.
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- 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.
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- 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).
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- 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.
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- A statistical hypothesis test is a method of making decisions using data from a scientific study.
- A statistical hypothesis test is a method of making decisions using data from a scientific study.
- Statistical hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis, which may not have pre-specified hypotheses.
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- One method of this is through cross-case analysis, which is analysis that involves an examination of more than one case.
- Cross-case analysis can be further broken down into variable-oriented analysis and case-oriented analysis.
- Deciding what is a variable, and how to code each subject on each variable, is more difficult in qualitative data analysis.
- It is more sophisticated in qualitative data analysis.
- Quantitative analysis of these codes is typically the capstone analytical step for this type of qualitative data.
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- In regression analysis, an interaction may arise when considering the relationship among three or more variables.
- In exploratory statistical analyses, it is common to use products of original variables as the basis of testing whether interaction is present with the possibility of substituting other more realistic interaction variables at a later stage.
- A simple setting in which interactions can arise is a two-factor experiment analyzed using Analysis of Variance (ANOVA).
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- Statistics deals with all aspects of the collection, organization, analysis, interpretation, and presentation of data.
- Statistics deals with all aspects of the collection, organization, analysis, interpretation, and presentation of data.
- Descriptive statistics and analysis of the new data tend to provide more information as to the truth of the proposition.
- This data can then be subjected to statistical analysis, serving two related purposes: description and inference.
- Statistical analysis of a data set often reveals that two variables of the population under consideration tend to vary together, as if they were connected.
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- One simple graph, thestem-and-leaf graphorstemplot, comes from the field of exploratory data analysis.It is a good choice when the data sets are small.
- To create the plot, divide each observation of data into a stem and a leaf.
- The stemplot is a quick way to graph and gives an exact picture of the data.
- An outlier is an observation of data that does not fit the rest of the data.
- Another type of graph that is useful for specific data values is a line graph.