Scatterplot
Scatterplot - Related Videos
07:26
Isolation of Functional Cardiac Immune Cells
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Cited by 9 •
2011
This method for isolating functional immune cells from the heart provides an alternative to the conventional methods of collagenase digestion, which causes unwanted immune cell activation, resulting in a decreased responsiveness of these cells. Our method of isolation yields functional cardiac immune cells by avoiding problems associated with enzymatic digestion.
04:36
Murine Superficial Lymph Node Surgery
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Cited by 10 •
2012
To follow the progression of an immune response over time within the same mouse, lymph nodes can be sequentially removed by surgery. Here, we describe how this technique can be performed.
02:20
Correlations
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2020
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
09:53
Automated Microfluidic Blood Lysis Protocol for Enrichment of Circulating Nucleated Cells
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Cited by 4 •
2009
An automated microfluidic device was developed for circulating nucleated cell enrichment from peripheral blood via erythrocyte lysis that ensures isolation of high quality sample without cell loss.
12:19
Analysis of Cell Cycle Position in Mammalian Cells
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Cited by 71 •
2012
Determining the cell cycle position of a population of cells, or understanding how signals affect proliferation, can be readily measured by flow cytometry using this protocol. We report a simple experimental approach to staining cells and quantifying their position in the cell cycle.
FAQs
Assigning the variables to the horizontal and vertical axes establishes how paired measurements should be read. The horizontal position identifies the value of one quantitative variable, while the vertical position identifies the corresponding value of the other. Keeping that assignment explicit helps readers interpret the direction and shape of any visible association.
A point pattern that rises from left to right suggests a positive association, whereas a pattern that falls suggests a negative one. Points showing no consistent direction indicate little association. The overall shape also matters: a curved arrangement may signal a nonlinear relationship, so a straight-line interpretation could miss the pattern.
Unusual observations deserve separate attention because they may appear far from the main point pattern. In statistical analysis, identifying these potential outliers helps researchers determine whether the relationship is consistent across the data and whether regression assumptions require closer evaluation. The graph flags observations for investigation without, by itself, explaining why they are unusual.
Clusters reveal that observations may form visually distinct groupings rather than one uniform pattern. Comparing those groupings can show whether different groups follow similar or contrasting relationships between the variables. This use extends interpretation beyond a single overall trend, allowing the scatterplot to support group comparisons during exploratory data analysis.
To construct one, select two quantitative variables with paired measurements, place one variable on the horizontal axis and the other on the vertical axis, and plot each pair as a single point. Then inspect the arrangement for direction, shape, clusters, and unusual observations. This sequence supports visual exploration before formal statistical modeling.
Scatterplots are especially useful before fitting a regression model. They let researchers check whether the data appear to follow a linear pattern and notice nonlinear structure or potential outliers that could complicate interpretation. Visual assessment does not replace formal analysis, but it helps identify which features of the data require attention during modeling.
A scatterplot keeps the individual paired observations visible while showing the direction, shape, clusters, and unusual points in their arrangement. This makes it useful for assessing correlation in context, supporting exploratory data analysis, and communicating how changes in one quantitative variable relate to another. The visual pattern can also guide subsequent formal analysis.