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Scatter plot                                                                                                                               1



     Scatter plot
                                                               Scatter plot




                                             One of the Seven Basic Tools of Quality
                            First described by Francis Galton

                            Purpose              To identify the type of relationship (if any) between two variables

     A scatter plot or scattergraph is a type of mathematical
     diagram using Cartesian coordinates to display values for
     two variables for a set of data.
     The data is displayed as a collection of points, each
     having the value of one variable determining the position
     on the horizontal axis and the value of the other variable
     determining the position on the vertical axis.[2] This kind
     of plot is also called a scatter chart, scatter diagram and
     scatter graph.


     Overview
     A scatter plot is used when a variable exists that is under
     the control of the experimenter. If a parameter exists that
     is systematically incremented and/or decremented by the
                                                                         Waiting time between eruptions and the duration of the eruption
     other, it is called the control parameter or independent
                                                                           for the Old Faithful Geyser in Yellowstone National Park,
     variable and is customarily plotted along the horizontal             Wyoming, USA. This chart suggests there are generally two
     axis. The measured or dependent variable is customarily                   "types" of eruptions: short-wait-short-duration, and
     plotted along the vertical axis. If no dependent variable                               long-wait-long-duration.

     exists, either type of variable can be plotted on either axis
     and a scatter plot will illustrate only the degree of correlation (not causation) between two variables.

     A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval.
     Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from
     lower        left     to        upper        right,        it      suggests        a        positive       correlation
Scatter plot                                                                                                                                                    2


     between the variables being studied. If the pattern of dots
     slopes from upper left to lower right, it suggests a
     negative correlation. A line of best fit (alternatively called
     'trendline') can be drawn in order to study the correlation
     between the variables. An equation for the correlation
     between the variables can be determined by established
     best-fit procedures. For a linear correlation, the best-fit
     procedure is known as linear regression and is guaranteed
     to generate a correct solution in a finite time.
     Unfortunately, no universal best-fit procedure is
     guaranteed to generate a correct solution for arbitrary
     relationships. A scatter plot is also very useful when
     we wish to see how two comparable data sets agree
     with each other. In this case, an identity line, i.e., a
     y=x line, or an 1:1 line, is often drawn as a reference.
     The more the two data sets agree, the more the                                      A 3D scatter plot allows for the visualization of multivariate data
                                                                                          of up to four dimensions. The Scatter plot takes multiple scalar
     scatters tend to concentrate in the vicinity of the
                                                                                           variables and uses them for different axes in phase space. The
     identity line; if the two data sets are numerically                                 different variables are combined to form coordinates in the phase
     identical, the scatters fall on the identity line exactly.                             space and they are displayed using glyphs and colored using
                                                                                                                                       [1]
                                                                                                              another scalar variable.
     One of the most powerful aspects of a scatter plot,
     however, is its ability to show nonlinear relationships between variables. Furthermore, if the data is represented by a
     mixture model of simple relationships, these relationships will be visually evident as superimposed patterns.
     The scatter diagram is one of the basic tools of quality control.[3]


     Example
     For example, to display values for "lung capacity" (first variable) and how long that person could hold his breath, a
     researcher would choose a group of people to study, then measure each one's lung capacity (first variable) and how
     long that person could hold his breath (second variable). The researcher would then plot the data in a scatter plot,
     assigning "lung capacity" to the horizontal axis, and "time holding breath" to the vertical axis.
     A person with a lung capacity of 400 ml who held his breath for 21.7 seconds would be represented by a single dot
     on the scatter plot at the point (400, 21.7) in the Cartesian coordinates. The scatter plot of all the people in the study
     would enable the researcher to obtain a visual comparison of the two variables in the data set, and will help to
     determine what kind of relationship there might be between the two variables.


     References
     [1] Visualizations that have been created with VisIt (https:/ / wci. llnl. gov/ codes/ visit/ gallery. html). at wci.llnl.gov. Last updated: November 8,
         2007.
     [2] Utts, Jessica M. Seeing Through Statistics 3rd Edition, Thomson Brooks/Cole, 2005, pp 166-167. ISBN 0-534-39402-7
     [3] Nancy R. Tague (2004). "Seven Basic Quality Tools" (http:/ / www. asq. org/ learn-about-quality/ seven-basic-quality-tools/ overview/
         overview. html). The Quality Toolbox. Milwaukee, Wisconsin: American Society for Quality. p. 15. . Retrieved 2010-02-05.



     External links
     • What is a scatterplot? (http://www.psychwiki.com/wiki/What_is_a_scatterplot?)
     • Correlation scatter-plot matrix - for ordered-categorical data (http://www.r-statistics.com/2010/04/
       correlation-scatter-plot-matrix-for-ordered-categorical-data/) - Explanation and R code
Article Sources and Contributors                                                                                                                                                              3



    Article Sources and Contributors
    Scatter plot  Source: http://en.wikipedia.org/w/index.php?oldid=414568862  Contributors: Acroterion, Ad1024, Alansohn, Alexius08, AndrewHowse, AugPi, Bento00, Bjoram11@yahoo.co.in,
    BoomerAB, CaAl, CardinalDan, Catalin Bogdan, Chris24, Claygate, Conscious, Cosmicfroggy, Courcelles, Crissov, Cryptic, Curtixlr8, Cyclopia, DVdm, Da monster under your bed,
    DanielPenfield, Den fjättrade ankan, Dimitrees, DoubleBlue, Downtown dan seattle, Dude1818, Epbr123, Epim, Eskimospy, Faithlessthewonderboy, Fangfufu, FelixKaiser, Funandtrvl, G716,
    Giftlite, Glenn macgougan, Gogo Dodo, Hgberman, Hooperbloob, Hu12, Hut 8.5, Ishikawa Minoru, Itai, JForget, Kadoo, Karnesky, Lambiam, Llygadebrill, Loodog, Mack2, Marianika,
    Martarius, Mathstat, McSly, Mdd, Melcombe, Mendaliv, Metacomet, Michael Hardy, Michael Snow, Micropw, Mitch Ames, Mmmmmmmmmm korn, Moorsmur, Nezzadar, Nigelovich,
    NightwolfAA2k5, Nishkid64, Nwstephens, Oleg Alexandrov, OnePt618, Orphan Wiki, Ph.eyes, Philip Trueman, Piotrus, Qwfp, RabidZombie, Rathinavelpec, Reyk, RodC, Roland Longbow,
    Ryk, Sbwoodside, ScaldingHotSoup, Snigbrook, Steinsky, Stevertigo, Sturm55, SueHay, TKD, Talgalili, Tedernst, VinceBowdren, VisFan, Watersidedoc, Whosasking, 152 anonymous edits




    Image Sources, Licenses and Contributors
    Image:Scatter diagram for quality characteristic XXX.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Scatter_diagram_for_quality_characteristic_XXX.svg  License: GNU Free
    Documentation License  Contributors: User:DanielPenfield
    Image:oldfaithful3.png  Source: http://en.wikipedia.org/w/index.php?title=File:Oldfaithful3.png  License: Public Domain  Contributors: Anynobody, Maksim, Mdd, Nandhp, Oleg Alexandrov,
    WikipediaMaster, 6 anonymous edits
    Image:Scatter plot.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Scatter_plot.jpg  License: Public Domain  Contributors: UCRL




    License
    Creative Commons Attribution-Share Alike 3.0 Unported
    http:/ / creativecommons. org/ licenses/ by-sa/ 3. 0/

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Scatter diagram

  • 1. Scatter plot 1 Scatter plot Scatter plot One of the Seven Basic Tools of Quality First described by Francis Galton Purpose To identify the type of relationship (if any) between two variables A scatter plot or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.[2] This kind of plot is also called a scatter chart, scatter diagram and scatter graph. Overview A scatter plot is used when a variable exists that is under the control of the experimenter. If a parameter exists that is systematically incremented and/or decremented by the Waiting time between eruptions and the duration of the eruption other, it is called the control parameter or independent for the Old Faithful Geyser in Yellowstone National Park, variable and is customarily plotted along the horizontal Wyoming, USA. This chart suggests there are generally two axis. The measured or dependent variable is customarily "types" of eruptions: short-wait-short-duration, and plotted along the vertical axis. If no dependent variable long-wait-long-duration. exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables. A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it suggests a positive correlation
  • 2. Scatter plot 2 between the variables being studied. If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the correlation between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct solution in a finite time. Unfortunately, no universal best-fit procedure is guaranteed to generate a correct solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree with each other. In this case, an identity line, i.e., a y=x line, or an 1:1 line, is often drawn as a reference. The more the two data sets agree, the more the A 3D scatter plot allows for the visualization of multivariate data of up to four dimensions. The Scatter plot takes multiple scalar scatters tend to concentrate in the vicinity of the variables and uses them for different axes in phase space. The identity line; if the two data sets are numerically different variables are combined to form coordinates in the phase identical, the scatters fall on the identity line exactly. space and they are displayed using glyphs and colored using [1] another scalar variable. One of the most powerful aspects of a scatter plot, however, is its ability to show nonlinear relationships between variables. Furthermore, if the data is represented by a mixture model of simple relationships, these relationships will be visually evident as superimposed patterns. The scatter diagram is one of the basic tools of quality control.[3] Example For example, to display values for "lung capacity" (first variable) and how long that person could hold his breath, a researcher would choose a group of people to study, then measure each one's lung capacity (first variable) and how long that person could hold his breath (second variable). The researcher would then plot the data in a scatter plot, assigning "lung capacity" to the horizontal axis, and "time holding breath" to the vertical axis. A person with a lung capacity of 400 ml who held his breath for 21.7 seconds would be represented by a single dot on the scatter plot at the point (400, 21.7) in the Cartesian coordinates. The scatter plot of all the people in the study would enable the researcher to obtain a visual comparison of the two variables in the data set, and will help to determine what kind of relationship there might be between the two variables. References [1] Visualizations that have been created with VisIt (https:/ / wci. llnl. gov/ codes/ visit/ gallery. html). at wci.llnl.gov. Last updated: November 8, 2007. [2] Utts, Jessica M. Seeing Through Statistics 3rd Edition, Thomson Brooks/Cole, 2005, pp 166-167. ISBN 0-534-39402-7 [3] Nancy R. Tague (2004). "Seven Basic Quality Tools" (http:/ / www. asq. org/ learn-about-quality/ seven-basic-quality-tools/ overview/ overview. html). The Quality Toolbox. Milwaukee, Wisconsin: American Society for Quality. p. 15. . Retrieved 2010-02-05. External links • What is a scatterplot? (http://www.psychwiki.com/wiki/What_is_a_scatterplot?) • Correlation scatter-plot matrix - for ordered-categorical data (http://www.r-statistics.com/2010/04/ correlation-scatter-plot-matrix-for-ordered-categorical-data/) - Explanation and R code
  • 3. Article Sources and Contributors 3 Article Sources and Contributors Scatter plot  Source: http://en.wikipedia.org/w/index.php?oldid=414568862  Contributors: Acroterion, Ad1024, Alansohn, Alexius08, AndrewHowse, AugPi, Bento00, Bjoram11@yahoo.co.in, BoomerAB, CaAl, CardinalDan, Catalin Bogdan, Chris24, Claygate, Conscious, Cosmicfroggy, Courcelles, Crissov, Cryptic, Curtixlr8, Cyclopia, DVdm, Da monster under your bed, DanielPenfield, Den fjättrade ankan, Dimitrees, DoubleBlue, Downtown dan seattle, Dude1818, Epbr123, Epim, Eskimospy, Faithlessthewonderboy, Fangfufu, FelixKaiser, Funandtrvl, G716, Giftlite, Glenn macgougan, Gogo Dodo, Hgberman, Hooperbloob, Hu12, Hut 8.5, Ishikawa Minoru, Itai, JForget, Kadoo, Karnesky, Lambiam, Llygadebrill, Loodog, Mack2, Marianika, Martarius, Mathstat, McSly, Mdd, Melcombe, Mendaliv, Metacomet, Michael Hardy, Michael Snow, Micropw, Mitch Ames, Mmmmmmmmmm korn, Moorsmur, Nezzadar, Nigelovich, NightwolfAA2k5, Nishkid64, Nwstephens, Oleg Alexandrov, OnePt618, Orphan Wiki, Ph.eyes, Philip Trueman, Piotrus, Qwfp, RabidZombie, Rathinavelpec, Reyk, RodC, Roland Longbow, Ryk, Sbwoodside, ScaldingHotSoup, Snigbrook, Steinsky, Stevertigo, Sturm55, SueHay, TKD, Talgalili, Tedernst, VinceBowdren, VisFan, Watersidedoc, Whosasking, 152 anonymous edits Image Sources, Licenses and Contributors Image:Scatter diagram for quality characteristic XXX.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Scatter_diagram_for_quality_characteristic_XXX.svg  License: GNU Free Documentation License  Contributors: User:DanielPenfield Image:oldfaithful3.png  Source: http://en.wikipedia.org/w/index.php?title=File:Oldfaithful3.png  License: Public Domain  Contributors: Anynobody, Maksim, Mdd, Nandhp, Oleg Alexandrov, WikipediaMaster, 6 anonymous edits Image:Scatter plot.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Scatter_plot.jpg  License: Public Domain  Contributors: UCRL License Creative Commons Attribution-Share Alike 3.0 Unported http:/ / creativecommons. org/ licenses/ by-sa/ 3. 0/