Scatter Charts in Graphical Representation: 10 Quotes
“Pencil and paper for construction of distributions, scatter diagrams, and run-charts to compare small groups and to detect trends are more efficient methods of estimation than statistical inference that depends on variances and standard errors, as the simple techniques preserve the information in the original data.” (William E Deming, “On Probability as Basis for Action” American Statistician Vol. 29 (4), 1975)
“As a general rule, plotted points and graph lines should be given more ‘weight’ than the axes. In this way the ‘meat’ will be easily distinguishable from the ‘bones’. Furthermore, an illustration composed of lines of unequal weights is always more attractive than one in which all the lines are of uniform thickness. It may not always be possible to emphasise the data in this way however. In a scattergram, for example, the more plotted points there are, the smaller they may need to be and this will give them a lighter appearance. Similarly, the more curves there are on a graph, the thinner the lines may need to be. In both cases, the axes may look better if they are drawn with a somewhat bolder line so that they are easily distinguishable from the data.” (Linda Reynolds & Doig Simmonds, “Presentation of Data in Science” 4th Ed, 1984)
“Scatter charts show the relationships between information, plotted as points on a grid. These groupings can portray general features of the source data, and are useful for showing where correlationships occur frequently. Some scatter charts connect points of equal value to produce areas within the grid which consist of similar features.” (Bruce Robertson, “How to Draw Charts & Diagrams”, 1988)
“The scatterplot is a useful exploratory method for providing a first look at bivariate data to see how they are distributed throughout the plane, for example, to see clusters of points, outliers, and so forth.” (William S Cleveland, “Visualizing Data”, 1993)
“Need to consider outliers as they can affect statistics such as means, standard deviations, and correlations. They can either be explained, deleted, or accommodated (using either robust statistics or obtaining additional data to fill-in). Can be detected by methods such as box plots, scatterplots, histograms or frequency distributions.” (Randall E Schumacker & Richard G Lomax, “A Beginner’s Guide to Structural Equation Modeling” 3rd Ed., 2010)
“[…] if you want to show change through time, use a time-series chart; if you need to compare, use a bar chart; or to display correlation, use a scatter-plot — because some of these rules make good common sense.” (Alberto Cairo, “The Functional Art”, 2011)
“Scatterplots are still the go-to visualization when one is examining relationships between continuous variables. One of the problems with the traditional scatterplot is that all data points are presented as if they are on equal footing. […] Bubble maps are scatterplots with added dimensions. The most common usage is to add weight to individual data points based on population.” (Christopher Lysy, “Developments in Quantitative Data Display and Their Implications for Evaluation”, 2013)
“A scatterplot reveals the strength and shape of the relationship between a pair of variables. A scatterplot represents the two variables by axes drawn at right angles to each other, showing the observations as a cloud of points, each point located according to its values on the two variables. Various lines can be added to the plot to help guide our search for understanding.” (Forrest W Young et al, “Visual Statistics: Seeing data with dynamic interactive graphics”, 2016)
“The most accurate but least interpretable form of data presentation is to make a table, showing every single value. But it is difficult or impossible for most people to detect patterns and trends in such data, and so we rely on graphs and charts. Graphs come in two broad types: Either they represent every data point visually (as in a scatter plot) or they implement a form of data reduction in which we summarize the data, looking, for example, only at means or medians.” (Daniel J Levitin, “Weaponized Lies”, 2017)
“Scatterplots are valuable because, without having to inspect each individual point, we can see overall aggregate patterns in potentially thousands of data points. But does this density of information come at a price — just how easy are they to read? […] The truth is such charts can shed light on complex stories in a way words alone — or simpler charts you might be more familiar with — cannot.” (Alan Smith, “How Charts Work: Understand and explain data with confidence”, 2022)
More quotes on “Scatter Chart” in Graphical Representation at sql-troubles.blogspot.com.
