Scatter diagram
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A scatter plot displays values for two variables from a data set as a collection of points, with one variable determining the horizontal position and the other determining the vertical position. Scatter plots can reveal correlations between variables, such as positive, negative, or no correlation. They are useful for visualizing nonlinear relationships and comparing two data sets. An example shows lung capacity on the x-axis and breath holding time on the y-axis for a study of individuals.
![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](https://image.slidesharecdn.com/scatterdiagram-110419224310-phpapp01/85/Scatter-diagram-1-320.jpg)
![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](https://image.slidesharecdn.com/scatterdiagram-110419224310-phpapp01/85/Scatter-diagram-2-320.jpg)
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/