| Row |
Level |
Rule Name |
Subject |
Property |
Value |
| 0 |
WARN
| annotation_whitespace |
STATO:0000011 |
IAO:0000119 |
adapted from Wolfram Alpha:
https://www.wolframalpha.com/input/?i=cartesian+coordinates&lk=4&num=6&lk=4&num=6
@en |
| 1 |
WARN
| annotation_whitespace |
STATO:0000023 |
IAO:0000119 |
A Dictionary of Statistics (2 rev ed.), OUP. ISBN-13: 9780199541454
http://www.oxfordreference.com/view/10.1093/acref/9780199541454.001.0001/acref-9780199541454-e-1588
@en |
| 2 |
WARN
| annotation_whitespace |
STATO:0000049 |
IAO:0000119 |
STATO, adapted from wikipedia (http://en.wikipedia.org/wiki/Hardy–Weinberg_principle)
|
| 3 |
WARN
| annotation_whitespace |
STATO:0000051 |
dc11:source |
NIST: http://www.itl.nist.gov/div898/handbook/eda/section3/eda366j.htm
@en |
| 4 |
WARN
| annotation_whitespace |
STATO:0000069 |
IAO:0000119 |
http://www.optique-ingenieur.org/en/courses/OPI_ang_M07_C01/co/Contenu_07.html@en |
| 5 |
WARN
| annotation_whitespace |
STATO:0000075 |
STATO:0000041 |
>library(vegan)
>rarefaction(x, subsample=5, plot=TRUE, color=TRUE, error=FALSE, legend=TRUE, symbol)
http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/library/vegan/html/vegan-package.html
@en |
| 6 |
WARN
| annotation_whitespace |
STATO:0000083 |
IAO:0000117 |
Philippe Rocca-Serra
@en |
| 7 |
WARN
| annotation_whitespace |
STATO:0000095 |
STATO:0000041 |
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/t.test.htmlt.test(dependent variable ~ independant variable, data = dataset, var.equal = FALSE, paired= TRUE)@en |
| 8 |
WARN
| annotation_whitespace |
STATO:0000100 |
IAO:0000119 |
adapted from http://htaglossary.net/standardised+mean+difference+(SMD)
@en |
| 9 |
WARN
| annotation_whitespace |
STATO:0000112 |
IAO:0000119 |
adapted from Wikipedia:
http://en.wikipedia.org/wiki/Funnel_plot
@en |
| 10 |
WARN
| annotation_whitespace |
STATO:0000120 |
STATO:0000041 |
http://cran.r-project.org/web/packages/beanplot/index.html@en |
| 11 |
WARN
| annotation_whitespace |
STATO:0000133 |
IAO:0000119 |
adapted from wikipedia: http://en.wikipedia.org/wiki/Post-hoc_analysis
last accessed: 2013-11-15
@en |
| 12 |
WARN
| annotation_whitespace |
STATO:0000143 |
IAO:0000119 |
adapted from wikipedia
@en |
| 13 |
WARN
| annotation_whitespace |
STATO:0000148 |
IAO:0000115 |
The Cochran-Armitage test is a statistical test used in categorical data analysis when the aim is to assess for the presence of an association between a dichotomous variable (variable with two categories) and a polychotomous variable (a variable with k categories).
The two-level variable represents the response, and the other represents an explanatory variable with ordered levels. The null hypothesis is the hypothesis of no trend, which means that the binomial proportion is the same for all levels of the explanatory variable
For example, doses of a treatment can be ordered as 'low', 'medium', and 'high', and we may suspect that the treatment benefit cannot become smaller as the dose increases. The trend test is often used as a genotype-based test for case-control genetic association studies.
@en |
| 14 |
WARN
| annotation_whitespace |
STATO:0000164 |
IAO:0000115 |
The interquartile range is a data item which corresponds to the difference between the upper quartile (3rd quartile) and lower quartile (1st quartile).
The interquartile range contains the second quartile or median.
The interquartile range is a data item providing a measure of data dispersion
@en |
| 15 |
WARN
| annotation_whitespace |
STATO:0000169 |
IAO:0000116 |
30/04/2014
- removed restriction:
'is about' exactly 2 'study group population'
- need more discussion for the relationship of fold change to study group populations for particular examples.
|
| 16 |
WARN
| annotation_whitespace |
STATO:0000184 |
IAO:0000115 |
A ratio is a data item which is formed with two numbers r and s is written r/s, where r is the numerator and s is the denominator. The ratio of r to s is equivalent to the quotient r/s.
@en |
| 17 |
WARN
| annotation_whitespace |
STATO:0000186 |
IAO:0000116 |
TODO: create 'inverse function' and replace 'data transformation' in the assertions
@en |
| 18 |
WARN
| annotation_whitespace |
STATO:0000188 |
IAO:0000119 |
adapted from wikipedia:
http://en.wikipedia.org/wiki/MA_plot
last accessed: 2014-03-13
@en |
| 19 |
WARN
| annotation_whitespace |
STATO:0000199 |
IAO:0000119 |
AGB-PRS, adapted from wikipedia (http://en.wikipedia.org/wiki/Mauchly's_sphericity_test)
polled on june,10th, 2013
and from R manual:
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/mauchly.test.html
@en |
| 20 |
WARN
| annotation_whitespace |
STATO:0000204 |
STATO:0000041 |
df(x, df1, df2, ncp, log = FALSE)
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/Fdist.html
@en |
| 21 |
WARN
| annotation_whitespace |
STATO:0000212 |
IAO:0000119 |
adapted from:
http://www.rasch.org/rmt/rmt193c.htm
and
http://en.wikipedia.org/wiki/Polychoric_correlation
@en |
| 22 |
WARN
| annotation_whitespace |
STATO:0000219 |
IAO:0000119 |
adapted from:
http://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/General_Information/qpcr_technical_guide.pdf
and
http://www.lifetechnologies.com/uk/en/home/life-science/pcr/real-time-pcr/qpcr-education/absolute-vs-relative-quantification-for-qpcr.html
@en |
| 23 |
WARN
| annotation_whitespace |
STATO:0000257 |
IAO:0000116 |
import from Population and Community Ontology:
http://www.ontobee.org/browser/rdf.php?o=PCO&iri=http://purl.obolibrary.org/obo/PCO_0000020
@en |
| 24 |
WARN
| annotation_whitespace |
STATO:0000263 |
IAO:0000115 |
Galbraith (Radial) plot is a scatter plot which can be used in the meta-analytic context to examine the data for heterogeneity. For a fixed-effects model, the plot shows the inverse of the standard errors on the horizontal axis against the individual observed effect sizes or outcomes standardized by their corresponding standard errors on the vertical axis.
Radial plots were introduced by Rex Galbraith (1988a, 1988b, 1994).
@en |
| 25 |
WARN
| annotation_whitespace |
STATO:0000263 |
STATO:0000041 |
http://www.inside-r.org/packages/cran/Luminescence/docs/plot_RadialPlotplot_RadialPlot(data, na.exclude = TRUE, negatives = \"remove\", log.z = TRUE, central.value, centrality = \"mean.weighted\", plot.ratio, bar.col, grid.col, legend.text, summary = FALSE, stats, line, line.col, line.label, output = FALSE, ...)@en |
| 26 |
WARN
| annotation_whitespace |
STATO:0000269 |
IAO:0000119 |
adapted from:
http://www.rasch.org/rmt/rmt193c.htm
and
http://en.wikipedia.org/wiki/Polychoric_correlation
@en |
| 27 |
WARN
| annotation_whitespace |
STATO:0000284 |
IAO:0000115 |
Breusch-Pagan test is a statistical test which computes a score test of the hypothesis of constant error variance against the alternative that the error variance changes with the level of the response (fitted values), or with a linear combination of predictors.
@en |
| 28 |
WARN
| annotation_whitespace |
STATO:0000284 |
STATO:0000041 |
http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/library/lmtest/html/bptest.htmlbptest(formula, varformula = NULL, studentize = TRUE, data = list())orhttp://www.inside-r.org/packages/cran/car/docs/ncvTest@en |
| 29 |
WARN
| annotation_whitespace |
STATO:0000285 |
IAO:0000112 |
http://www.ncbi.nlm.nih.gov/pubmed/?term=17182697Bioinformatics. 2007 Feb 15;23(4):401-7.Enrichment or depletion of a GO category within a class of genes: which test?Rivals I1, Personnaz L, Taing L, Potier MC.@en |
| 30 |
WARN
| annotation_whitespace |
STATO:0000289 |
IAO:0000112 |
let's consider an experiment evaluating 2 compounds (aspirin & ibuprofen) at 3 distinct dose levels (low, medium, high) and 4 time points post exposure (0h, 6h, 12h, 24h). Assuming the treatments are applied only once (no replication), the number of observation in a full factorial design is 2 x 3 x 4 = 24 so the design matrix would have 24 rows and 3 columns (1 per factor (independent variable).
@en |
| 31 |
WARN
| annotation_whitespace |
STATO:0000289 |
STATO:0000041 |
model.matrix(object, data = environment(object),
contrasts.arg = NULL, xlev = NULL, ...)
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/model.matrix.html
@en |
| 32 |
WARN
| annotation_whitespace |
STATO:0000298 |
IAO:0000119 |
adapted from:
http://en.wikipedia.org/wiki/Binomial_test
@en |
| 33 |
WARN
| annotation_whitespace |
STATO:0000301 |
IAO:0000115 |
The covariance is a measurement data item about the strength of correlation between a set (2 or more) of random variables.
The covariance is obtained by forming:
cov(X,Y)=E([X-E(X)][Y-E(Y)] where E(X), E(Y) is the expected value (mean) of variable X and Y respectively.
covariance is symmetric so cov(X,Y)=cov(Y,X).
The covariance is usefull when looking at the variance of the sum of the 2 random variables since:
var(X+Y) = var(X) +var(Y) +2cov(X,Y)
The covariance cov(x,y) is used to obtain the coefficient of correlation cor(x,y) by normalizing (dividing) cov(x,y) but the product of the standard deviations of x and y.
@en |
| 34 |
WARN
| annotation_whitespace |
STATO:0000303 |
IAO:0000119 |
adapted from:
http://en.wikipedia.org/wiki/Student's_t-test#Independent_.28unpaired.29_samples
and from:
http://www.psychology.emory.edu/clinical/bliwise/Tutorials/TOM/meanstests/tind.htm
@en |
| 35 |
WARN
| annotation_whitespace |
STATO:0000319 |
IAO:0000119 |
adapted from :
http://en.wikipedia.org/wiki/Effect_size#Cohen.27s_d
and
http://blog.stata.com/tag/cohens-d/
@en |
| 36 |
WARN
| annotation_whitespace |
STATO:0000320 |
IAO:0000119 |
adapted from :
http://en.wikipedia.org/wiki/Effect_size#Cohen.27s_d
and
http://blog.stata.com/tag/cohens-d/
@en |
| 37 |
WARN
| annotation_whitespace |
STATO:0000325 |
IAO:0000115 |
The Akaike information criterion (AIC) is a measure of the relative quality of a statistical model for a given set of data. As such, AIC provides a means for model selection. AIC is defined as:
AIC = 2K - 2log(L)
where K is the number of predictors and L is the maximized likelihood value.
AIC deals with the trade-off between the goodness of fit of the model and the complexity of the model. It is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. AIC does not provide a test of a model in the sense of testing a null hypothesis; i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.
@en |
| 38 |
WARN
| annotation_whitespace |
STATO:0000377 |
IAO:0000119 |
http://en.wikipedia.org/wiki/Deviance_%28statistics%29 |
| 39 |
WARN
| annotation_whitespace |
STATO:0000389 |
IAO:0000115 |
a power-law probability distribution is a probability distribution whose density function (or mass function in the discrete case) has the form
p(x) = L(x) . x^{-alpha}
where alpha is a parameter >1 and L(x) is a slowly varying function.
@en |
| 40 |
WARN
| annotation_whitespace |
STATO:0000393 |
IAO:0000115 |
the Pareto type-II probability distribution is a continuous probability distribution which is defined by a probability density function characterized by 2 parameters, alpha and lambda, 2 real, strictly positive numbers. alpha is known as the shape parameter while lambda is known as the scale parameter.
the function defines the probably of a continous random variable according to the following:
p(x) = {\alpha \over \lambda} \left[{1+ {x \over \lambda}}\right]^{-(\alpha+1)}, \qquad x \geq 0,
@en |
| 41 |
WARN
| annotation_whitespace |
STATO:0000397 |
STATO:0000041 |
http://personality-project.org/r/html/harmonic.mean.htmlUsage: > harmonic.mean(x,na.rm=TRUE)Arguments: x, a vector, matrix, or data.framena.rm, na.rm=TRUE remove NA values before processing@en |
| 42 |
WARN
| annotation_whitespace |
STATO:0000422 |
IAO:0000115 |
The L’Abbé plot was introduced in 1987 in the context of meta-analyses of clinical trials with dichotomous (binary) outcomes, as a plot of observed risks in the treatment group against observed risks in the control group.
Another formulation is that it plots the event rate in the experimental (intervention) group against the event rate in the control group, as an aid to exploring the heterogeneity of effect estimates within a meta-analysis.
It is diagram used in meta-analysis that compares the risks observed in the experimental and control arms of clinical trials. Each trial is located in the space of a diagram where the sizes of the circles indicate the sizes of the trials. Trials in which the experimental treatment had a higher risk than the control will be in the upper left of the plot. If risk in the both groups is the same the circle will fall on the line of equality. If the control treatment has a higher risk than the experimental treatment then the point will be in the lower right of the plot. It is often used as an indicator of heterogeneity and hence as an indicator of the likelihood that results from different trials can be validly combined. Named after Kristin L'Abbé.
@en |
| 43 |
WARN
| annotation_whitespace |
STATO:0000423 |
IAO:0000119 |
adapted from:
http://handbook.cochrane.org/chapter_9/9_2_2_4_measure_of_absolute_effect_the_risk_difference.htm
@en |
| 44 |
WARN
| annotation_whitespace |
STATO:0000434 |
IAO:0000115 |
Cochran's Q test is a statistical test used for unreplicated randomized block design experiments with a binary response variable and paired data.
In the analysis of two-way randomized block designs where the response variable can take only two possible outcomes (coded as 0 and 1), Cochran's Q test is a non-parametric statistical test to verify whether k treatments have identical effects.
@en |
| 45 |
WARN
| annotation_whitespace |
STATO:0000440 |
STATO:0000041 |
dixon.outliers(data)
from:
http://finzi.psych.upenn.edu/library/referenceIntervals/html/dixon.outliers.html
@en |
| 46 |
WARN
| annotation_whitespace |
STATO:0000442 |
STATO:0000041 |
FindOutliersTietjenMooreTest(dataSeries,k,alpha=0.05)
from:
https://rdrr.io/rforge/climtrends/man/findOutliers.Tietjen.Moore.test.html
@en |
| 47 |
WARN
| annotation_whitespace |
STATO:0000443 |
STATO:0000041 |
rgrubbs.test(x, alpha = 0.05)
from:
http://finzi.psych.upenn.edu/library/OutlierDM/html/rgrubbs.test.html
@en |
| 48 |
WARN
| annotation_whitespace |
STATO:0000445 |
IAO:0000115 |
a split split plot design is a study design where restricted randomization affect 2 study factors (and not 1 as in split-plot design). Such design is only possible if at least 3 independent variables are present.
@en |
| 49 |
WARN
| annotation_whitespace |
STATO:0000445 |
IAO:0000119 |
adapted from https://onlinecourses.science.psu.edu/stat503/node/72
last accessed 2016/12/15
@en |
| 50 |
WARN
| annotation_whitespace |
STATO:0000446 |
IAO:0000115 |
Restricted randomization is a kind of randomization which is used or occured when hard to change factors exist in a study design. In other words, when complete randomization is not possible, a case of restricted randomization exists, for instance in the case of split-plot design.
Restricted randomization allows intuitively poor allocations of treatments to experimental units to be avoided, while retaining the theoretical benefits of randomization.
Restricted randomization can also result from an unplanned event and is then something that should be avoided. RandomizeR R package can be used to detect such events and assess the quality of randomization process.
@en |
| 51 |
WARN
| annotation_whitespace |
STATO:0000446 |
IAO:0000119 |
Adapted from Wikipedia:
https://en.wikipedia.org/wiki/Restricted_randomization
last accessed: 2016/12/15
@en |
| 52 |
WARN
| annotation_whitespace |
http://purl.obolibrary.org/obo/stato.owl |
dc11:rights |
This Ontology is distributed under a Creative Commons Attribution License ^^http://www.w3.org/2001/XMLSchema#anyURI |
| 53 |
WARN
| duplicate_label_synonym |
STATO:0000239 |
IAO:0000118 |
high throughput screening@en |
| 54 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000027 |
OBI:0000417 |
STATO:0000121 |
| 55 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000033 |
OBI:0000312 |
OBI:0200117 |
| 56 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000046 |
BFO:0000051 |
STATO:0000223 |
| 57 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000046 |
STATO:0000001 |
STATO:0000248 |
| 58 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000085 |
OBI:0000295 |
STATO:0000175 |
| 59 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000119 |
OBI:0000299 |
STATO:0000144 |
| 60 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000131 |
OBI:0000417 |
STATO:0000183 |
| 61 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000133 |
BFO:0000062 |
OBI:0200201 |
| 62 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000137 |
OBI:0000417 |
STATO:0000226 |
| 63 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000191 |
OBI:0000417 |
STATO:0000224 |
| 64 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000202 |
OBI:0000417 |
STATO:0000253 |
| 65 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000247 |
OBI:0000417 |
STATO:0000173 |
| 66 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000279 |
OBI:0000417 |
STATO:0000255 |
| 67 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000337 |
OBI:0000299 |
STATO:0000485 |
| 68 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000443 |
OBI:0000417 |
STATO:0000439 |
| 69 |
WARN
| equivalent_class_axiom_no_genus |
STATO:0000471 |
STATO:0000403 |
STATO:0000039 |
| 70 |
WARN
| missing_definition |
STATO:0000342 |
IAO:0000115 |
|
| 71 |
WARN
| missing_definition |
STATO:0000344 |
IAO:0000115 |
|
| 72 |
WARN
| missing_definition |
STATO:0000345 |
IAO:0000115 |
|
| 73 |
WARN
| missing_definition |
STATO:0000380 |
IAO:0000115 |
|
| 74 |
WARN
| missing_definition |
STATO:0000381 |
IAO:0000115 |
|
| 75 |
WARN
| missing_definition |
STATO:0000382 |
IAO:0000115 |
|
| 76 |
WARN
| multiple_equivalent_classes |
STATO:0000046 |
owl:equivalentClass |
blank node |
| 77 |
WARN
| multiple_equivalent_classes |
STATO:0000137 |
owl:equivalentClass |
blank node |
| 78 |
INFO
| lowercase_definition |
STATO:0000001 |
IAO:0000115 |
property to indicate that a design declares a variable; the inverse property is 'is declared by'@en |
| 79 |
INFO
| lowercase_definition |
STATO:0000002 |
IAO:0000115 |
an electronic file is an information content entity which conforms to a specification or format and which is meant to hold data and information in digital form, accessible to software agents@en |
| 80 |
INFO
| lowercase_definition |
STATO:0000003 |
IAO:0000115 |
a balanced design is a an experimental design where all experimental group have the an equal number of subject observations@en |
| 81 |
INFO
| lowercase_definition |
STATO:0000004 |
IAO:0000115 |
property to indicate the variables declared by a design; the inverse property is 'declares'@en |
| 82 |
INFO
| lowercase_definition |
STATO:0000005 |
IAO:0000115 |
a single factor design is a study design which declares exactly 1 independent variable@en |
| 83 |
INFO
| lowercase_definition |
STATO:0000006 |
IAO:0000115 |
x-axis is a cartesian coordinate axis which is orthogonal to the y-axis and the z-axis@en |
| 84 |
INFO
| lowercase_definition |
STATO:0000007 |
IAO:0000115 |
an axis is a line graph used as reference line for the measurement of coordinates.@en |
| 85 |
INFO
| lowercase_definition |
STATO:0000008 |
IAO:0000115 |
y-axis is a cartesian coordinate axis which is orthogonal to the x-axis and the z-axis@en |
| 86 |
INFO
| lowercase_definition |
STATO:0000011 |
IAO:0000115 |
a cartesian axis is one of 3 the axis in a cartesian coordinate system defining a referential in 3 dimensions. each of the axis is orthogonal to the other 2@en |
| 87 |
INFO
| lowercase_definition |
STATO:0000012 |
IAO:0000115 |
z-axis is a cartesian coordinate axis which is orthogonal to the x-axis and the y-axis@en |
| 88 |
INFO
| lowercase_definition |
STATO:0000013 |
IAO:0000115 |
a 2 dimensional cartesian coordinate system is a cartesian coordinate system which defines 2 orthogonal one dimensional axes and which may be used to describe a 2 dimensional spatial region. |
| 89 |
INFO
| lowercase_definition |
STATO:0000019 |
IAO:0000115 |
normal distribution hypothesis is a goodness of fit hypothesis stating that the distribution computed from the sample population fits a normal distribution.@en |
| 90 |
INFO
| lowercase_definition |
STATO:0000021 |
IAO:0000115 |
a confidence interval which covers 90% of the sampling distribution, meaning that there is a 90% risk of false positive (type I error)@en |
| 91 |
INFO
| lowercase_definition |
STATO:0000024 |
IAO:0000115 |
a three dimensional cartesian coordinate system is a cartesian coordinate system which defines 3 orthogonal one dimensional axes and which may be used to describe a 3 dimensional spatial region. |
| 92 |
INFO
| lowercase_definition |
STATO:0000027 |
IAO:0000115 |
linkage between 2 categorical variable test is a statistical test which evaluates if there is an association between a predictor variable assuming discrete values and a response variable also assuming discrete values@en |
| 93 |
INFO
| lowercase_definition |
STATO:0000028 |
IAO:0000115 |
measure of variation or statistical dispersion is a data item which describes how much a theoritical distribution or dataset is spread.@en |
| 94 |
INFO
| lowercase_definition |
STATO:0000029 |
IAO:0000115 |
a measure of central tendency is a data item which attempts to describe a set of data by identifying the value of its centre.@en |
| 95 |
INFO
| lowercase_definition |
STATO:0000031 |
IAO:0000115 |
binary classification (or binomial classification) is a data transformation which aims to cast members of a set into 2 disjoint groups depending on whether the element have a given property/feature or not.@en |
| 96 |
INFO
| lowercase_definition |
STATO:0000032 |
IAO:0000115 |
an alternative term used for STATO statistical ontology and ISA team@en |
| 97 |
INFO
| lowercase_definition |
STATO:0000034 |
IAO:0000115 |
a model parameter is a data item which is part of a model and which is meant to characterize an theoritecal or unknown population. a model parameter may be estimated by considering the properties of samples presumably taken from the theoritecal population@en |
| 98 |
INFO
| lowercase_definition |
STATO:0000035 |
IAO:0000115 |
the range is a measure of variation which describes the difference between the lowest score and the highest score in a set of numbers (a data set) |
| 99 |
INFO
| lowercase_definition |
STATO:0000038 |
IAO:0000115 |
a set of 2 subjects which result from a pairing process which assigns subject to a set based on a pairing rule/criteria@en |
| 100 |
INFO
| lowercase_definition |
STATO:0000039 |
IAO:0000115 |
a statistic is a measurement datum to describe a dataset or a variable. It is generated by a calculation on set of observed data.@en |
| 101 |
INFO
| lowercase_definition |
STATO:0000040 |
IAO:0000115 |
an MA plot is a scatter plot of the log intensity ratios M = log_2(T/R) versus the average log intensities A = log_2(T*T)/2, where T and R represent the signal intensities in the test and reference channels respectively.@en |
| 102 |
INFO
| lowercase_definition |
STATO:0000041 |
IAO:0000115 |
a R command syntax or link to a R documentation in support of Statistical Ontology Classes or Data Transformations@en |
| 103 |
INFO
| lowercase_definition |
STATO:0000043 |
IAO:0000115 |
a false positive rate whose value is 5 per cent@en |
| 104 |
INFO
| lowercase_definition |
STATO:0000044 |
IAO:0000115 |
one-way anova is an analysis of variance where the different groups being compared are associated with the factor levels of only one independent variable. The null hypothesis is an absence of difference between the means calculated for each of the groups. The test assumes normality and equivariance of the data.@en |
| 105 |
INFO
| lowercase_definition |
STATO:0000045 |
IAO:0000115 |
two-way anova is an analysis of variance where the different groups being compared are associated the factor levels of exatly 2 independent variables. The null hypothesis is an absence of difference between the means calculated for each of the groups. The test assumes normality and equivariance of the data.@en |
| 106 |
INFO
| lowercase_definition |
STATO:0000046 |
IAO:0000115 |
a block design is a kind of study design which declares a blocking variable (also known as nuisance variable) in order to account for a known source of variation and reduce its impact on the acquisition of the signal@en |
| 107 |
INFO
| lowercase_definition |
STATO:0000047 |
IAO:0000115 |
a count is a data item denoted by an integer and represented the number of instances or occurences of an entity@en |
| 108 |
INFO
| lowercase_definition |
STATO:0000050 |
IAO:0000115 |
signal to noise ratio is a measurement datum comparing the amount of meaningful, useful or interesting data (the signal) to the amount of irrelevant or false data (the noise). Depending on the field and domain of application, different variables will be used to determinate a 'signal to noise ratio'. In statistics, the definition of signal to noise ratio is the ratio of the mean of a measurement to its standard deviation. It thus corresponds to the inverse of the coefficient of variation@en |
| 109 |
INFO
| lowercase_definition |
STATO:0000053 |
IAO:0000115 |
a false positive rate is a data item which accounts for the proportion of incorrect rejection of a true null hypothesis.@en |
| 110 |
INFO
| lowercase_definition |
STATO:0000054 |
IAO:0000115 |
homoskedasticity states that all variances under consideration are homogenous.@en |
| 111 |
INFO
| lowercase_definition |
STATO:0000055 |
IAO:0000115 |
chromosome coordinate system is a genomic coordinate which uses chromosome of a particular assembly build process to define start and end positions. This coordinate system is unstable and will change with each new genome sequence assembly build.@en |
| 112 |
INFO
| lowercase_definition |
STATO:0000056 |
IAO:0000115 |
a null hypothesis which states that no linkage exists between 2 categorical variables@en |
| 113 |
INFO
| lowercase_definition |
STATO:0000058 |
IAO:0000115 |
goodness of fit hypothesis is a null hypothesis stating that the distribution computed from the sample population fits a theoretical distribution or that a dataset can be correctly explained by a model@en |
| 114 |
INFO
| lowercase_definition |
STATO:0000059 |
IAO:0000115 |
the Student's t distribution is a continuous probability distribution which arises when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown.@en |
| 115 |
INFO
| lowercase_definition |
STATO:0000060 |
IAO:0000115 |
hypergeometric distribution is a probability distribution that describes the probability of k successes in n draws from a finite population of size N containing K successes without replacement@en |
| 116 |
INFO
| lowercase_definition |
STATO:0000062 |
IAO:0000115 |
is a null hypothesis stating that there are no difference observed across a series of measurements made one same subject.@en |
| 117 |
INFO
| lowercase_definition |
STATO:0000063 |
IAO:0000115 |
genomic coordinate datum is a data item which denotes a genomic position expressed using a genomic coordinate system@en |
| 118 |
INFO
| lowercase_definition |
STATO:0000064 |
IAO:0000115 |
sequence read count is a data item determining how many sequence reads generated by a DNA sequencing assay for a given stretch of DNA can counted |
| 119 |
INFO
| lowercase_definition |
STATO:0000067 |
IAO:0000115 |
a continuousprobability distribution is a probability distribution which is defined by a probability density function@en |
| 120 |
INFO
| lowercase_definition |
STATO:0000071 |
IAO:0000115 |
reaction rate is a measurement datum which represents the speed of a chemical reaction turning reactive species into product species of event (i.e the number of such conversions)s occuring over a time interval@en |
| 121 |
INFO
| lowercase_definition |
STATO:0000072 |
IAO:0000115 |
substrate concentration is a scalar measurement datum which denotes the amount of molecular entity involved in an enzymatic reaction (or catalytic chemical reaction) and whose role in that reaction is as substrate.@en |
| 122 |
INFO
| lowercase_definition |
STATO:0000075 |
IAO:0000115 |
a rarefaction curve is a graph used for estimating species richness in ecology studies@en |
| 123 |
INFO
| lowercase_definition |
STATO:0000080 |
IAO:0000115 |
the Brown Forsythe test is a statistical test which evaluates if the variance of different groups are equal. It relies on computing the median rather than the mean, as used in the Levene's test for homoschedacity.
This test maybe used to, for instance, ensure that the conditions of applications of ANOVA are met.@en |
| 124 |
INFO
| lowercase_definition |
STATO:0000082 |
IAO:0000115 |
a fixed effect model is a statistical model which represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random.@en |
| 125 |
INFO
| lowercase_definition |
STATO:0000084 |
IAO:0000115 |
multinomial logistic regression model is a model which attempts to explain data distribution associated with *polychotomous* response/dependent variable in terms of values assumed by the independent variable uses a function of predictor/independent variable(s): the function used in this instance of regression modeling is probit function.@en |
| 126 |
INFO
| lowercase_definition |
STATO:0000085 |
IAO:0000115 |
effect size estimate is a data item about the direction and strength of the consequences of a causative agent as explored by statistical methods. Those methods produce estimates of the effect size, e.g. confidence interval@en |
| 127 |
INFO
| lowercase_definition |
STATO:0000086 |
IAO:0000115 |
an F-test is a statistical test which evaluates that the computed test statistics follows an F-distribution under the null hypothesis. The F-test is sensitive to departure from normality. F-test arise when decomposing the variability in a data set in terms of sum of squares.@en |
| 128 |
INFO
| lowercase_definition |
STATO:0000087 |
IAO:0000115 |
a polychotomous variable is a categorical variable which is defined to have minimally 2 categories or possible values@en |
| 129 |
INFO
| lowercase_definition |
STATO:0000088 |
IAO:0000115 |
statistical sample size is a count evaluating the number of individual experimental units@en |
| 130 |
INFO
| lowercase_definition |
STATO:0000089 |
IAO:0000115 |
a case-control study design is a observation study design which assess the risk of particular outcome (a trait or a disease) associated with an event (either an exposure or endogenous factor). A case-control study design therefore declares an exposure variable which is dichotomous in nature (exposed/non-exposed) and an outcome variable, which is also dichotomous (case or control), thus giving the name to the design. During the execution of the design, a case control study defines a population and counts the events to determine their frequency.@en |
| 131 |
INFO
| lowercase_definition |
STATO:0000090 |
IAO:0000115 |
a dichotomous variable is a categorical variable which is defined to have only 2 categories or possible values@en |
| 132 |
INFO
| lowercase_definition |
STATO:0000095 |
IAO:0000115 |
paired t-test is a statistical test which is specifically designed to analysis differences between paired observations in the case of studies realizing repeated measures design with only 2 repeated measurements per subject (before and after treatment for example)@en |
| 133 |
INFO
| lowercase_definition |
STATO:0000096 |
IAO:0000115 |
stratification is a planned process which executes a stratification rule using as input a population and assign it member to mutually exclusive subpopulation based on the values defined by the stratification rule@en |
| 134 |
INFO
| lowercase_definition |
STATO:0000099 |
IAO:0000115 |
a random effect(s) model, also called a variance components model, is a kind of hierarchical linear model. It assumes that the dataset being analysed consists of a hierarchy of different populations whose differences relate to that hierarchy.@en |
| 135 |
INFO
| lowercase_definition |
STATO:0000100 |
IAO:0000115 |
standardized mean difference is data item computed by forming the difference between two means, divided by an estimate of the within-group standard deviation.
It is used to provide an estimatation of the effect size between two treatments when the predictor (independent variable) is categorical and the response(dependent) variable is continuous@en |
| 136 |
INFO
| lowercase_definition |
STATO:0000101 |
IAO:0000115 |
the relationship between a fraction and the number above the line@en |
| 137 |
INFO
| lowercase_definition |
STATO:0000102 |
IAO:0000115 |
relationship between a planned process and the plan specification that it carries out; it is defined as equivalent to the composed relationship (realizes o concretizes)@en |
| 138 |
INFO
| lowercase_definition |
STATO:0000103 |
IAO:0000115 |
the multinomial distribution is a probability distribution which gives the probability of any particular combination of numbers of successes for various categories defined in the context of n independent trials each of which leads to a success for exactly one of k categories, with each category having a given fixed success probability.@en |
| 139 |
INFO
| lowercase_definition |
STATO:0000105 |
IAO:0000115 |
log signal intensity ratio is a data item which corresponding the logarithmitic base 2 of the ratio between 2 signal intensity, each corresponding to a condition.@en |
| 140 |
INFO
| lowercase_definition |
STATO:0000106 |
IAO:0000115 |
probit regression model is a model which attempts to explain data distribution associated with *dichotomous* response/dependent variable in terms of values assumed by the independent variable uses a function of predictor/independent variable(s): the function used in this instance of regression modeling is the probit function aka the quantile function, i.e., the inverse cumulative distribution function (CDF), associated with the standard normal distribution.@en |
| 141 |
INFO
| lowercase_definition |
STATO:0000107 |
IAO:0000115 |
a statistical model is an information content entity which is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more other variables. The model is statistical as the variables are not deterministically but stochastically related.@en |
| 142 |
INFO
| lowercase_definition |
STATO:0000108 |
IAO:0000115 |
linear regression model is a model which attempts to explain data distribution associated with response/dependent variable in terms of values assumed by the independent variable uses a linear function or linear combination of the regression parameters and the predictor/independent variable(s).
linear regression modeling makes a number of assumptions, which includes homoskedasticity (constance of variance)@en |
| 143 |
INFO
| lowercase_definition |
STATO:0000109 |
IAO:0000115 |
multinomial logistic regression model is a model which attempts to explain data distribution associated with *polychotomous* response/dependent variable in terms of values assumed by the independent variable uses a function of predictor/independent variable(s): the function used in this instance of regression modeling is logistic function.@en |
| 144 |
INFO
| lowercase_definition |
STATO:0000111 |
IAO:0000115 |
a sequence read is a DNA sequence data which is generated by a DNA sequencer@en |
| 145 |
INFO
| lowercase_definition |
STATO:0000112 |
IAO:0000115 |
a Funnel plot is a scatter plot of treatment effect versus a measure of study size and aims to provide a visual aid to detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely. An asymmetric funnel indicates a relationship between treatment effect and study size.
Known caveats: If high precision studies really are different from low precision studies with respect to effect size (e.g., due to different populations examined) a funnel plot may give a wrong impression of publication bias. The appearance of the funnel plot can change quite dramatically depending on the scale on the y-axis — whether it is the inverse square error or the trial size.
Funnel plot was introduced by Light and Palmer in 1984.@en |
| 146 |
INFO
| lowercase_definition |
STATO:0000113 |
IAO:0000115 |
variance is a data item about a random variable or probability distribution. it is equivalent to the square of the standard deviation. It is one of several descriptors of a probability distribution, describing how far the numbers lie from the mean (expected value).The variance is the second moment of a distribution.@en |
| 147 |
INFO
| lowercase_definition |
STATO:0000114 |
IAO:0000115 |
relationship between an element and a set it belongs to@en |
| 148 |
INFO
| lowercase_definition |
STATO:0000115 |
IAO:0000115 |
relationship between a set and one of its elements@en |
| 149 |
INFO
| lowercase_definition |
STATO:0000116 |
IAO:0000115 |
the process of using statistical analysis for interpreting and communicating \"what the data say\".@en |
| 150 |
INFO
| lowercase_definition |
STATO:0000117 |
IAO:0000115 |
a discrete probability distribution is a probability distribution which is defined by a probability mass function where the random variable can only assume a finite number of values or infinitely countable values@en |
| 151 |
INFO
| lowercase_definition |
STATO:0000118 |
IAO:0000115 |
ranking is a data transformation which turns a non-ordinal variable into a Ordinal variable by sorting the values of the input variable and replacing their value by their position in the sorting result@en |
| 152 |
INFO
| lowercase_definition |
STATO:0000119 |
IAO:0000115 |
model parameter estimation is a data transformation that finds parameter values (the model parameter estimates) most compatible with the data as judged by the model.@en |
| 153 |
INFO
| lowercase_definition |
STATO:0000120 |
IAO:0000115 |
beanplot is a plot in which (one or) multiple batches (\"beans\") are shown. Each bean consists of a density trace, which is mirrored to
form a polygon shape. Next to that, a one-dimensional scatter plot shows all the individual measurements, like in a stripchart.
The name beanplot stems from green beans. The density shape can be seen as the pod of a green bean, while the scatter plot shows the seeds inside the pod.@en |
| 154 |
INFO
| lowercase_definition |
STATO:0000121 |
IAO:0000115 |
the objective of a data transformation to evaluate a null hypothesis of absence of linkage between variables.@en |
| 155 |
INFO
| lowercase_definition |
STATO:0000122 |
IAO:0000115 |
a pedigree chart is a graph which plots parent child relations@en |
| 156 |
INFO
| lowercase_definition |
STATO:0000123 |
IAO:0000115 |
r2 is a correlation coefficient which is computed over the frequency of 2 dichotomous variable and is used as a measure of Linkage Disequilibrium and as input data item to the creation of an LD plot@en |
| 157 |
INFO
| lowercase_definition |
STATO:0000124 |
IAO:0000115 |
a stratification rule/criteria is a criteria used to determine population strata so that a stratification process implementing the rule can result in any member of the total population being assigned to one and only one stratum@en |
| 158 |
INFO
| lowercase_definition |
STATO:0000126 |
IAO:0000115 |
volcano plot is a kind of scatter plot which graphs the negative log of the p-value (significance) on the y-axis versus log2 of fold-change between 2 conditions on the x-axis.
It is a popular method for visualizing differential occurence of variables between 2 conditions.@en |
| 159 |
INFO
| lowercase_definition |
STATO:0000127 |
IAO:0000115 |
a confidence interval which covers 99% of the sampling distribution, meaning that there is a 1% risk of false positive (type I error)@en |
| 160 |
INFO
| lowercase_definition |
STATO:0000130 |
IAO:0000115 |
the Breslow-Day test is a statistical test which evaluates if the odds ratios are homogenous across N 2x2 contingency tables, for instance several 2x2 contingency tables associated with different strata of a stratified population when evaluating the relationship between exposure and outcome or associated with the different samples coming from several centres in a multicentric study in clinical trial context.@en |
| 161 |
INFO
| lowercase_definition |
STATO:0000131 |
IAO:0000115 |
a sphericity test is a null hypothesis statistical testing procedure which posits a null hypothesis of equality of the variances of the differences between levels of the repeated measures factor@en |
| 162 |
INFO
| lowercase_definition |
STATO:0000134 |
IAO:0000115 |
specificity is a measurement datum qualifying a binary classification test and is computed by substracting the false positive rate to the integral numeral 1@en |
| 163 |
INFO
| lowercase_definition |
STATO:0000135 |
IAO:0000115 |
strictly standardized mean difference (SSMS) is a standardized mean difference which corresponds to the ratio of mean to the standard deviation of the difference between two groups.
SSMD directly measures the magnitude of difference between two groups.
SSMD is widely used in High Content Screen for hit selection and quality control.
When the data is preprocessed using log-transformation as normally done in HTS experiments, SSMD is the mean of log fold change divided by the standard deviation of log fold change with respect to a negative reference.
In other words, SSMD is the average fold change (on the log scale) penalized by the variability of fold change (on the log scale).
For quality control, one index for the quality of an HTS assay is the magnitude of difference between a positive control and a negative reference in an assay plate. For hit selection, the size of effects of a compound (i.e., a small molecule or an siRNA) is represented by the magnitude of difference between the compound and a negative reference. SSMD directly measures the magnitude of difference between two groups. Therefore, SSMD can be used for both quality control and hit selection in HTS experiments.@en |
| 164 |
INFO
| lowercase_definition |
STATO:0000137 |
IAO:0000115 |
an homoskedasticity test is a statistical test aiming at evaluate if the variances from several random samples are similar@en |
| 165 |
INFO
| lowercase_definition |
STATO:0000138 |
IAO:0000115 |
a 2x2 contingency table is a contingency table build for 2 dichotomous variables (i.e. 2 categorical variables, each with only 2 possible outcomes). It is the simplest of contingency tables@en |
| 166 |
INFO
| lowercase_definition |
STATO:0000139 |
IAO:0000115 |
a subject pairing is a planned process which executes a pairing rule and results in the creation of sets of 2 subjects meeting the pairing criteria@en |
| 167 |
INFO
| lowercase_definition |
STATO:0000140 |
IAO:0000115 |
a contigency table is a data item which displays the (multivariate) frequency distribution of the possible values of categorical variables.
The first row of the table corresponds to categories of one categorical variable, the first column of the table corresponds to categories of the other categorical variable, the cells corresponding to each combination of categories is filled with the observed occurences in the sample being considered.
The table also contains marginal total (marginal sums) and grand total of the occurences
The term contingency table was first used by Karl Pearson in \"On the Theory of Contingency and Its Relation to Association and Normal Correlation\", part of the Drapers' Company Research Memoirs Biometric Series I published in 1904.@en |
| 168 |
INFO
| lowercase_definition |
STATO:0000141 |
IAO:0000115 |
acute toxicity study is an investigation which use interventions organized according to a factorial design and a parallel group design to observe the effect of use of high dose xenobiotics in animal models or cellular models@en |
| 169 |
INFO
| lowercase_definition |
STATO:0000144 |
IAO:0000115 |
a model parameter estimate is a data item which results from a model parameter estimation process and which provides a numerical value about a model parameter.@en |
| 170 |
INFO
| lowercase_definition |
STATO:0000145 |
IAO:0000115 |
the geometric distribution is a negative binomial distribution where r is 1.
It is useful for modeling the runs of consecutive successes (or failures) in repeated independent trials of a system.
The geometric distribution models the number of successes before one failure in an independent succession of tests where each test results in success or failure.
The geometric distribution with prob = p has density
p(x) = p (1-p)^x
for x = 0, 1, 2, …, 0 < p ≤ 1.
If an element of x is not integer, the result of dgeom is zero, with a warning.
The quantile is defined as the smallest value x such that F(x) ≥ p, where F is the distribution function.@en |
| 171 |
INFO
| lowercase_definition |
STATO:0000146 |
IAO:0000115 |
a null hypothesis stating that there are differences observed between group of subjects@en |
| 172 |
INFO
| lowercase_definition |
STATO:0000149 |
IAO:0000115 |
binomial logistic regression model is a model which attempts to explain data distribution associated with *dichotomous* response/dependent variable in terms of values assumed by the independent variable uses a function of predictor/independent variable(s): the function used in this instance of regression modeling is logistic function.@en |
| 173 |
INFO
| lowercase_definition |
STATO:0000150 |
IAO:0000115 |
a minimum value is a data item which denotes the smallest value found in a dataset or resulting from a calculation.@en |
| 174 |
INFO
| lowercase_definition |
STATO:0000151 |
IAO:0000115 |
maximum value is a data item which denotes the largest value found in a dataset or resulting from a calculation.@en |
| 175 |
INFO
| lowercase_definition |
STATO:0000152 |
IAO:0000115 |
a quartile is a quantile which splits data into sections accrued of 25% of data, so the first quartile delineates 25% of the data, the second quartile delineates 50% of the data and the third quartile, 75 % of the data@en |
| 176 |
INFO
| lowercase_definition |
STATO:0000154 |
IAO:0000115 |
a violin plot is a plot combining the features of box plot and kernel density plot. The violin plot is therefore similar to box plot but it incorporated in the display the probability density of the data at different values.
Typically violin plots will include a marker for the median of the data and a box indicating the interquartile range, as in standard box plots.@en |
| 177 |
INFO
| lowercase_definition |
STATO:0000155 |
IAO:0000115 |
meta-analysis is a data transformation which uses the effect size estimates from several independent quantitative scientific studies addressing the same question in order to assess finding consistency.@en |
| 178 |
INFO
| lowercase_definition |
STATO:0000156 |
IAO:0000115 |
the Scheffe test is a data transformation which evaluates all possible contrasts and adjusting the levels significance by accounting for multiple comparison. The test is therefore conservative. Confidence intervals can be constructed for the corresponding linear regression. It was developped by American statistician Henry Scheffe in 1959.@en |
| 179 |
INFO
| lowercase_definition |
STATO:0000157 |
IAO:0000115 |
the LSD test is a statistical test for multiple comparisons of treatments by means of least significant difference following an ANOVA analysis |
| 180 |
INFO
| lowercase_definition |
STATO:0000158 |
IAO:0000115 |
a null hypothesis which states that a linkage exists between 2 categorical variables@en |
| 181 |
INFO
| lowercase_definition |
STATO:0000161 |
IAO:0000115 |
variable distribution is data item which denotes the spatial resolution of data point making up a variable. variable distribution may be compared to a known probability distribution using goodness of fit test or plotting a quantile-quantile plot for visual assessment of the fit.@en |
| 182 |
INFO
| lowercase_definition |
STATO:0000162 |
IAO:0000115 |
the role played by an entity part of study group as defined by an experimental design and realized in a data analysis and data interpretation@en |
| 183 |
INFO
| lowercase_definition |
STATO:0000163 |
IAO:0000115 |
trimmed mean or truncated mean is a measure of central tendency which involves the calculation of the mean after discarding given parts of a probability distribution or sample at the high and low end, and typically discarding an equal amount of both@en |
| 184 |
INFO
| lowercase_definition |
STATO:0000165 |
IAO:0000115 |
a pie chart is a graph in which a circular graph is divided into sector illustrating numerical proportion, meaning that the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents.@en |
| 185 |
INFO
| lowercase_definition |
STATO:0000166 |
IAO:0000115 |
the bart chart is a graph resulting from plotting rectangular bars with lengths proportional to the values that they represent. |
| 186 |
INFO
| lowercase_definition |
STATO:0000167 |
IAO:0000115 |
the first quartile is a quartile which splits the lower 25 % of the data@en |
| 187 |
INFO
| lowercase_definition |
STATO:0000168 |
IAO:0000115 |
a real time quantitative pcr plot is a line graph which plots the signal fluorescence intensity as a function of the number of PCR cycle@en |
| 188 |
INFO
| lowercase_definition |
STATO:0000170 |
IAO:0000115 |
the first quartile is a quartile which splits the 75 % of the data@en |
| 189 |
INFO
| lowercase_definition |
STATO:0000172 |
IAO:0000115 |
expected fragments per kilobase of transcript per million fragments mapped is a metric used to report transcript expression event as generated by RNA-Seq using paired-end library. The calculated value results from 2 types of normalization, one to take into account the difference in reads counts associated with transcript length (at equal abundance, longer transcripts will have more reads than shorter transcripts) , (hence the 'per kilobase of transcript') and the other one to take into account different sequencing depth during distinct sequencing runs (hence the 'per millions mapped fragment'. The metric is specifically produced by cufflink software.@en |
| 190 |
INFO
| lowercase_definition |
STATO:0000173 |
IAO:0000115 |
homogeneity testing objective is the objective of a data transformation to test a null hypothesis that two or more sub-groups of a population share the same distribution of a single categorical variable.
For example, do people of different countries have the same proportion of smokers to non-smokers@en |
| 191 |
INFO
| lowercase_definition |
STATO:0000175 |
IAO:0000115 |
confidence interval calculation is a data transformation which determines a confidence interval for a given statistical parameter@en |
| 192 |
INFO
| lowercase_definition |
STATO:0000176 |
IAO:0000115 |
t-statistic is a statistic computed from observations and used to produce a p-value in statistical test when compared to a Student's t distribution.@en |
| 193 |
INFO
| lowercase_definition |
STATO:0000177 |
IAO:0000115 |
the beta distribution is a continuous probability distributions defined on the interval [0, 1] parametrized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution@en |
| 194 |
INFO
| lowercase_definition |
STATO:0000180 |
IAO:0000115 |
standard normal distribution is a normal distribution with variance = 1 and mean=0@en |
| 195 |
INFO
| lowercase_definition |
STATO:0000183 |
IAO:0000115 |
sphericity testing objective is a statistical objective of a data transformation which aims to test a null hypothesis of sphericity holds.@en |
| 196 |
INFO
| lowercase_definition |
STATO:0000185 |
IAO:0000115 |
a 2 by n contingency table is a contingency table built for one dichotomous variable (a categorical variable with only 2 outcomes) and one polychotomous variable (a polychomotomous variable with at least 2 outcomes)@en |
| 197 |
INFO
| lowercase_definition |
STATO:0000188 |
IAO:0000115 |
average log signal intensity is a data time which corresponds to the sum of 2 distinct logarithm base 2 transformed signal intensity, each corresponding to a distinct condition of signal acquisition, divided by 2.@en |
| 198 |
INFO
| lowercase_definition |
STATO:0000191 |
IAO:0000115 |
a goodness of fit statistical test is a statistical test which aim to evaluate if a sample distribution can be considered equivalent to a theoretical distribution used as input@en |
| 199 |
INFO
| lowercase_definition |
STATO:0000192 |
IAO:0000115 |
a cartesian product is a data transformation which operates on a n Sets to produce a set of all possible ordered n-tuples where each element of the tuple comes from a Set |