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A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. [1] It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials .
Forest inventory is the systematic collection of data and forest information for assessment or analysis. An estimate of the value and possible uses of timber is an important part of the broader information required to sustain ecosystems .
[2] [5] [6] Examples of appropriate visualizations include the scatter plot for regression, and Gardner–Altman plots for two independent groups. [27] While historical data-group plots (bar charts, box plots, and violin plots) do not display the comparison, estimation plots add a second axis to explicitly visualize the effect size.
Line plot survey is a systematic sampling technique used on land surfaces for laying out sample plots within a rectangular grid to conduct forest inventory or agricultural research. It is a specific type of systematic sampling , similar to other statistical sampling methods such as random sampling , but more straightforward to carry out in ...
Plots play an important role in statistics and data analysis. The procedures here can broadly be split into two parts: quantitative and graphical. Quantitative techniques are a set of statistical procedures that yield numeric or tabular output. Examples of quantitative techniques include: [1] hypothesis testing; analysis of variance
In statistics, a Galbraith plot (also known as Galbraith's radial plot or just radial plot) is one way of displaying several estimates of the same quantity that have different standard errors. [1] Example for Galbraith's radial plot. It can be used to examine heterogeneity in a meta-analysis, as an alternative or supplement to a forest plot.
Rarefaction analysis assumes that the individuals in an environment are randomly distributed, the sample size is sufficiently large, that the samples are taxonomically similar, and that all of the samples have been performed in the same manner. If these assumptions are not met, the resulting curves will be greatly skewed. [8]
For n trees, QMD is calculated using the quadratic mean formula: where is the diameter at breast height of the i th tree. Compared to the arithmetic mean, QMD assigns greater weight to larger trees – QMD is always greater than or equal to arithmetic mean for a given set of trees.