Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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THE BHP WIRE —
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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 coded (color/shape/size), 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. The scatter diagram is one of the seven basic tools of quality control.
In statistics, a Q–Q plot (quantile–quantile plot) is a probability plot, a graphical method for comparing two probability distributions by plotting their quantiles against each other. A point (x, y) on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of the first distribution (x-coordinate). This defines a parametric curve where the parameter is the index of the quantile interval. If the two distributions being compared are similar, the points in the Q–Q plot will approximately lie on the identity line y = x. If the distributions are linearly related, the points in the Q–Q plot will approximately lie on a line, but not necessarily on the line y = x. Q–Q plots can also be used as a graphical means of estimating parameters in a location-scale family of distributions. A Q–Q plot is used to compare the shapes of distributions, providing a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions. Q–Q plots can be used to compare collections of data, or theoretical distributions. The use of Q–Q plots to compare two samples of data can be viewed as a non-parametric approach to comparing their underlying distributions. A Q–Q plot is generally more diagnostic than comparing the samples' histograms, but is less widely known. Q–Q plots are commonly used to compare a data set to a theoretical model. This can provide an assessment of goodness of fit that is graphical, rather than reducing to a numerical summary statistic. Since Q–Q plots compare distributions, there is no need for the values to be observed as pairs, as in a scatter plot, or even for the numbers of values in the two groups being compared to be equal. The term "probability plot" sometimes refers specifically to a Q–Q plot, sometimes to a more general class of plots, and sometimes to the less commonly used P–P plot. The probability plot correlation coefficient plot (PPCC plot) is a quantity derived from the idea of Q–Q plots, which measures the agreement of a fitted distribution with observed data and which is sometimes used as a means of fitting a distribution to data.
In statistics, a volcano plot is a type of scatter-plot that is used to quickly identify changes in large data sets composed of replicate data. It plots significance versus fold-change on the y and x axes, respectively. These plots are increasingly common in omic experiments such as genomics, proteomics, and metabolomics where one often has a list of many thousands of replicate data points between two conditions and one wishes to quickly identify the most meaningful changes. A volcano plot combines a measure of statistical significance from a statistical test (e.g., a p value from an ANOVA model) with the magnitude of the change, enabling quick visual identification of those data-points (genes, etc.) that display large magnitude changes that are also statistically significant. A volcano plot is a sophisticated data visualization tool used in statistical and genomic analyses to illustrate the relationship between the magnitude of change and statistical significance. It is constructed by plotting the negative logarithm (base 10) of the p-value on the y-axis, ensuring that data points with lower p-values—indicative of higher statistical significance—are positioned toward the top of the plot. The x-axis represents the logarithm of the fold change between two conditions, allowing for a symmetric representation of both upregulated and downregulated changes relative to the center. This transformation ensures that equivalent deviations in either direction are equidistant from the origin, facilitating intuitive interpretation. The plot inherently highlights two critical regions of interest: data points that reside in the upper extremes of the graph while being significantly displaced to the left or right. These points correspond to variables that exhibit both substantial fold changes (magnitude of effect) and exceptional statistical significance, making them prime candidates for further investigation in differential analyses. The volcano plot, therefore, serves as a powerful means of identifying key biomarkers, differentially expressed genes, or other significant entities within complex datasets. Additional information can be added by coloring the points according to a third dimension of data (such as signal intensity), but this is not uniformly employed. Volcano plots are also used to graphically display a significance analysis of microarrays (SAM) gene selection criterion, an example of regularization. The concept of volcano plot can be generalized to other applications, where the x axis is related to a measure of the strength of a statistical signal, and y axis is related to a measure of the statistical significance of the signal. For example, in a genetic association case-control study, such as genome-wide association study, a point in a volcano plot represents a single-nucleotide polymorphism. Its x value can be the logarithm of the odds ratio and its y value can be -log10 of the p value from a Chi-square test or a Chi-square test statistic. Volcano plots show a characteristic upwards two arm shape because the x axis, i.e. the underlying log2-fold changes, are generally normal distributed whereas the y axis, the log10-p values, tend toward greater significance for fold-changes that deviate more strongly from zero. The density of the normal distribution takes the form y = e − x 2 {\displaystyle y=e^{-x^{2}}} . So the ln {\displaystyle \ln } of that is ln ( y ) = − x 2 {\displaystyle \ln(y)=-x^{2}} and the negative ln {\displaystyle \ln } is − ln ( y ) = x 2 {\displaystyle -\ln(y)=x^{2}} which is a parabola whose arms reach upwards on the right and left sides. The upper bound of the data is one parabola and the lower bound is another parabola.
A rug plot is a plot of data for a single quantitative variable, displayed as marks along an axis. It is used to visualise the distribution of the data. As such it is analogous to a histogram with zero-width bins, or a one-dimensional scatter plot. Rug plots are often used in combination with two-dimensional scatter plots by placing a rug plot of the x values of the data along the x-axis, and similarly for the y values. This is the origin of the term "rug plot", as these rug plots with perpendicular markers look like tassels along the edges of the rectangular "rug" of the scatter plot.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Brown v. Board of Education in 1954.
