Scatter plot - wikipedia Scatter plot Not to be confused with Correlogram. Scatter plot First described by John Herschel Purpose To identify the type of relationship ( if any ) between two quantitative variables Waiting time between eruptions and the duration of the eruption for the Old Faithful Geyser in Yellowstone National Park, Wyoming, USA. This chart suggests there are generally two `` types '' of eruptions : short - wait - short - duration, and long - wait - long - duration. A 3D scatter plot allows the visualization of multivariate data. This scatter plot takes multiple scalar variables and uses them for different axes in phase space. The different variables are combined to form coordinates in the phase space and they are displayed using glyphs and colored using another scalar variable. A scatter plot ( also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram ) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are color - coded, one additional variable can be displayed. The data are 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. Contents 1 Overview 2 Example 3 Scatterplot matrices 4 References 5 External links Overview A scatter plot can be used either when one continuous variable that is under the control of the experimenter and the other depends on it or when both continuous variables are independent. If a parameter exists that is systematically incremented and / or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable 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. For example, weight and height, weight would be on y axis and height would be on the x axis. Correlations may be positive ( rising ), negative ( falling ), or null ( uncorrelated ). If the pattern of dots slopes from lower left to upper right, it indicates a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it indicates a negative correlation. A line of best fit ( alternatively called ' trendline ' ) can be drawn in order to study the relationship 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. 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 to show nonlinear relationships between variables. The ability to do this can be enhanced by adding a smooth line such as LOESS. Furthermore, if the data are represented by a mixture model of simple relationships, these relationships will be visually evident as superimposed patterns. The scatter diagram is one of the seven basic tools of quality control. Scatter charts can be built in the form of bubble, marker, or / and line charts. Example For example, to display a link between a person 's lung capacity, and how long that person could hold his / her 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 / her 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 cl who held his / her 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. Scatterplot matrices For a set of data variables ( dimensions ) X, X,..., X, the scatter plot matrix shows all the pairwise scatter plots of the variables on a single view with multiple scatterplots in a matrix format. For k variables, the scatterplot matrix will contain k rows and k columns. A plot located on the intersection of i - th row and j - th column is a plot of variables X versus X. This means that each row and column is one dimension, and each cell plots a scatterplot of two dimensions. A generalized scatterplot matrix offers a range of displays of paired combinations of categorical and quantitative variables. A mosaic plot, fluctuation diagram, or faceted bar chart may be used to display two categorical variables. Other plots are used for one categorical and one quantitative variables. Vizualization of 3D data along with the correspondent scatterplot matrix References Jump up ^ Friendly, Michael ; Denis, Dan ( 2005 ). `` The early origins and development of the scatterplot ''. Journal of the History of the Behavioral Sciences. 41 ( 2 ) : 103 -- 130. doi : 10.1002 / jhbs. 20078. Jump up ^ Visualizations that have been created with VisIt at wci.llnl.gov. Last updated : November 8, 2007. Jump up ^ Jarrell, Stephen B. ( 1994 ). Basic Statistics ( Special pre-publication ed. ). Dubuque, Iowa : Wm. C. Brown Pub. p. 492. ISBN 0 - 697 - 21595 - 4. When we search for a relationship between two variables, a standard graph of the available data pairs ( X, Y ), called a scatter diagram, frequently helps... Jump up ^ Utts, Jessica M. Seeing Through Statistics 3rd Edition, Thomson Brooks / Cole, 2005, pp 166 - 167. ISBN 0 - 534 - 39402 - 7 Jump up ^ Cleveland, William ( 1993 ). Visualizing data. Murray Hill, N.J. Summit, N.J : At & T Bell Laboratories Published by Hobart Press. ISBN 978 - 0963488404. Jump up ^ Nancy R. Tague ( 2004 ). `` Seven Basic Quality Tools ''. The Quality Toolbox. Milwaukee, Wisconsin : American Society for Quality. p. 15. Retrieved 2010 - 02 - 05. Jump up ^ `` Scatter Chart - AnyChart JavaScript Chart Documentation ''. AnyChart. Retrieved 3 February 2016. Jump up ^ Scatter Plot Matrix at itl.nist.gov. Jump up ^ Emerson, John W. ; Green, Walton A. ; Schoerke, Barret ; Crowley, Jason ( 2013 ). `` The Generalized Pairs Plot ''. Journal of Computational and Graphical Statistics. 22 ( 1 ) : 79 -- 91. doi : 10.1080 / 10618600.2012. 694762. External links Wikimedia Commons has media related to Scatterplots. What is a scatterplot ? Correlation scatter - plot matrix for ordered - categorical data -- Explanation and R code Density scatterplot for large datasets ( hundreds of millions of points ) Seven Basic Tools of Quality Cause - and - effect diagram Check sheet Control chart Histogram Pareto chart Scatter diagram Stratification Quality ( business ) Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L - moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product - moment correlation Rank correlation Spearman 's rho Kendall 's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q -- Q plot Run chart Scatter plot Stem - and - leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model L space Parameter location scale shape Parametric family Likelihood ( monotone ) Location -- scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M - estimator Minimum distance Unbiased estimators Mean - unbiased minimum - variance Rao -- Blackwellization Lehmann -- Scheffé theorem Median unbiased Plug - in Interval estimation Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife Testing hypotheses 1 - & 2 - tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons Parametric tests Likelihood - ratio Wald Score Specific tests Z - test ( normal ) Student 's t - test F - test Goodness of fit Chi - squared G - test Kolmogorov -- Smirnov Anderson -- Darling Lilliefors Jarque -- Bera Normality ( Shapiro -- Wilk ) Likelihood - ratio test Model selection Cross validation AIC BIC Rank statistics Sign Sample median Signed rank ( Wilcoxon ) Hodges -- Lehmann estimator Rank sum ( Mann -- Whitney ) Nonparametric anova 1 - way ( Kruskal -- Wallis ) 2 - way ( Friedman ) Ordered alternative ( Jonckheere -- Terpstra ) Bayesian inference Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis Correlation Pearson product - moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines ( MARS ) Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard predictors Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity Generalized linear model Exponential families Logistic ( Bernoulli ) / Binomial / Poisson regressions Partition of variance Analysis of variance ( ANOVA, anova ) Analysis of covariance Multivariate ANOVA Degrees of freedom Categorical / Multivariate / Time - series / Survival analysis Categorical Cohen 's kappa Contingency table Graphical model Log - linear model McNemar 's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal Time - 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Question: in order to draw a scatter diagram we need interval data for
Answer: one continuous variable