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Statistical Graphics
Statistical graphics allow results to be displayed in some sort of pictorial form and include scatter plots, histograms, and box plots.
Learning Objective

Recognize the techniques used in exploratory data analysis
Key Points

Graphical statistical methods explore the content of a data set.

Graphical statistical methods are used to find structure in data.

Graphical statistical methods check assumptions in statistical models.

Graphical statistical methods communicate the results of an analysis.

Graphical statistical methods communicate the results of an analysis.
Terms

histogram
a representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval

scatter plot
A type of display using Cartesian coordinates to display values for two variables for a set of data.

box plot
A graphical summary of a numerical data sample through five statistics: median, lower quartile, upper quartile, and some indication of more extreme upper and lower values.
Full Text
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. They include plots such as scatter plots , histograms, probability plots, residual plots, box plots, block plots and biplots.
Exploratory data analysis (EDA) relies heavily on such techniques. They can also provide insight into a data set to help with testing assumptions, model selection and regression model validation, estimator selection, relationship identification, factor effect determination, and outlier detection. In addition, the choice of appropriate statistical graphics can provide a convincing means of communicating the underlying message that is present in the data to others.
Graphical statistical methods have four objectives:
• The exploration of the content of a data set
• The use to find structure in data
• Checking assumptions in statistical models
• Communicate the results of an analysis.
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. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18^{th} century. Statistical graphics developed through attention to four problems:
• Spatial organization in the 17^{th} and 18^{th} century
• Discrete comparison in the 18^{th} and early 19^{th} century
• Continuous distribution in the 19^{th} century and
• Multivariate distribution and correlation in the late 19^{th} and 20^{th} century.
Since the 1970s statistical graphics have been reemerging as an important analytic tool with the revitalisation of computer graphics and related technologies.
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Key Term Reference
 bivariate
 Appears in this related concepts: Graphing Bivariate Relationships, Introduction to Bivariate Data, and Exercises
 block
 Appears in this related concepts: Random Sampling, Comparing Three or More Populations: Randomized Block Design, and Principles of experimental design
 correlation
 Appears in this related concepts: Standard Error, Benefits of Globalization, and Descriptive and Correlational Statistics
 datum
 Appears in this related concepts: Change of Scale, Comparing Nested Models, and Controlling for a Variable
 distribution
 Appears in this related concepts: Application of Knowledge, Interpreting Distributions Constructed by Others, and Selling to Consumers
 exploratory data analysis
 Appears in this related concepts: Exploratory Data Analysis (EDA), Elements of a Hypothesis Test, and References
 factor
 Appears in this related concepts: Randomized Design: SingleFactor, The Perceptual Process, and Solving Quadratic Equations by Factoring
 mean
 Appears in this related concepts: Mean, Variance, and Standard Deviation of the Binomial Distribution, The Mean Value Theorem, Rolle's Theorem, and Monotonicity, and Understanding Statistics
 outlier
 Appears in this related concepts: Median, Outliers, and Fitting a Curve
 plot
 Appears in this related concepts: Graphs for Quantitative Data, Plotting Points on a Graph, and Using a Statistical Calculator
 probability
 Appears in this related concepts: Particle in a Box, The Addition Rule, and Theoretical Probability
 quantitative
 Appears in this related concepts: Averages of Qualitative and Ranked Data, Quantitative or Qualitative Data?, and Evaluating GDP as a Measure of the Economy
 regression
 Appears in this related concepts: Making a Box Model, Coefficient of Determination, and Two Regression Lines
 residual
 Appears in this related concepts: Plotting the Residuals, Models with Both Quantitative and Qualitative Variables, and Degrees of Freedom
 residuals
 Appears in this related concepts: The Correction Factor, Inferences of Correlation and Regression, and Introduction to line fitting, residuals, and correlation
 statistics
 Appears in this related concepts: Applications of Statistics, What Is Statistics?, and Population Demography
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Cite This Source
Source: Boundless. “Statistical Graphics.” Boundless Statistics. Boundless, 03 Jul. 2014. Retrieved 28 Apr. 2015 from https://www.boundless.com/statistics/textbooks/boundlessstatisticstextbook/visualizingdata3/graphingdata19/statisticalgraphics904409/