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Topic: Univariate

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	Univariate and Bivariate Tables (TABLES)
		The main use of TABLES is to obtain univariate or bivariate frequency tables with optional row, column and corner percentages and optional univariate and bivariate statistics.
		Both univariate frequencies and cumulative univariate frequencies may be generated for any number of input variables and may also be expressed as percentages of the weighted or unweighted total frequency.
		Univariate and bivariate frequencies are always printed for all codes in the data whether or not they represent missing data.
	www.unesco.org /webworld/portal/idams/html/english/E1tables.htm (3561 words)

	univariate statistics
		Univariate analysis explores each variable in a data set, separately.
		Univariate descriptive statistics can summarize large quantities of numerical data and reveal patterns in the raw data.
		Many health statistics are expressed as rates, for example, the birth rate is the number of births per some population, such as number of births per 1,000 women.
	www.csulb.edu /~msaintg/ppa696/696uni.htm (928 words)

	Eight Features: Wuensch Response
		To illustrate why univariate distributions are boring compared to relationships between variables, suppose that as statisticians we are interested in some real-world variable, which I shall call Y. We can empirically study Y in two different ways: 1.
		That is, studying the univariate distribution of ANY variable Y is rigorously equivalent (mathematically and empiri- cally) to studying the relationship between Y and a set of pre- dictor variables in the limiting case when the number of predic- tor variables is reduced to zero.
		However, univariate distributions obvi- ously DO provide a social payoff (i.e., a basis for action) in one important situation -- the situation in which an action deci- sion is made on the basis of the univariate distribution of some variable.
	www.matstat.com /teach/p0039.htm (3403 words)

	PA 765: Univariate GLM, ANOVA, and ANCOVA
		Univariate GLM is the version of the general linear model now often used to implement two long-established statistical procedures - ANOVA and ANCOVA.
		Univariate GLM, ANOVA, and ANCOVA all deal with the situation where there is one dependent variable and one or more independents.
		If you ask for parameter estimates for a model which has factors as well as covariates, adding the b corresponding to a given level of a factor when added to the b for the intercept, gives the estimate of the dependent when the covariate is 0.
	www2.chass.ncsu.edu /garson/pa765/anova.htm (15037 words)

	The UNIVARIATE Procedure : CLASS Statement
		PROC UNIVARIATE creates a separate plot for the analysis variable values in each cell of the cross-classification and arranges these component plots in a matrix to form the comparative plot with uniform horizontal and vertical axes.
		PROC UNIVARIATE first determines the bin size and midpoints for the key cell, and then extends the midpoint list to accommodate the data ranges for the remaining cells.
		By default, PROC UNIVARIATE sorts the levels of the first variable in the order that is determined by its ORDER= option and, within each of these levels, it sorts the levels of the second variable in the order that is determined by its ORDER= option.
	www.asu.edu /sas/sasdoc/sashtml/proc/z1236721.htm (1369 words)

	The UNIVARIATE Procedure : Statistical Computations
		When you specify the NORMAL option in the PROC UNIVARIATE statement or you request a fitted parametric distribution in the HISTOGRAM statement, the procedure computes test statistics for the null hypothesis that the values of the analysis variable are a random sample from the specified theoretical distribution.
		PROC UNIVARIATE uses a modified Kolmogorov D statistic to test the data against a normal distribution with mean and variance equal to the sample mean and variance.
		Likewise, PROC UNIVARIATE assumes that the SIGMA= parameter, which determines the upper threshold (SIGMA) for the beta distribution, is known.
	v8doc.sas.com /sashtml/proc/zte-comp.htm (4285 words)

	Annotated Output of Proc Univariate
		In our example, we will use the hsb2 data set and we will investigate the distribution of the continuous variable write, which is the scores of 200 high school students on a writing test.
		We use the plots option on the proc univariate statement to produce the stem-and-leaf and normal probability plots shown at the bottom of the output.
		If there are missing values for the variable, proc univariate will output the statistics about the missing values, such as the number and the percentage of missing values.
	www.ats.ucla.edu /stat/sas/output/univ.htm (2267 words)

	EPA - Univariate Methods
		Univariate methods include tests that compare samples from different groups.
		Typically univariate methods are designed to evaluate 1 variable at a time, although a second variable used to group or sort the variables may be implied.
		For example, a single variable, such as number of eutrophic diatoms, may be grouped by a second variable, such as presence or absence of agriculture in the watershed.
	www.epa.gov /bioindicators/statprimer/univariate.html (299 words)

	Model Generation
		For example, one of the univariate models we generate describes how load imbalance varies as a function of P when D=128.
		Using this approach, we are able to produce a model for each cross-tuple, by combining all the univariate models for that tuple according to the best fit for their interactions.
		This window shows the average determination coefficient for each univariate model representing a factor and overhead category pair (along the top row of values), and for the multivariate model (the bottom row of values).
	www.cs.rochester.edu /u/meira/prop/node50.html (1039 words)

	6.4.4. Univariate Time Series Models (Site not responding. Last check: 2007-11-01)
		The term "univariate time series" refers to a time series that consists of single (scalar) observations recorded sequentially over equal time increments.
		Although a univariate time series data set is usually given as a single column of numbers, time is in fact an implicit variable in the time series.
		If the data are equi-spaced, the time variable, or index, does not need to be explicitly given.
	www.itl.nist.gov /div898/handbook/pmc/section4/pmc44.htm (129 words)

	New Tools for Analyzing Distributions
		PROC UNIVARIATE now computes both Winsorized and trimmed means as well as robust measures of scale including Gini's mean difference, the mean absolute deviation (MAD), and the Sn and Qn statistics.
		Other new PROC UNIVARIATE options enable you to specify the location parameter value in the null hypothesis of a test of location, request a table of all possible modes, and control the number of tabulated extreme values and observations.
		By default, PROC UNIVARIATE displays the scale of the vertical axis as the percentage of observations per bin.
	support.sas.com /rnd/app/da/new/daunivariate.html (751 words)

	1.2.1. Underlying Assumptions
		The simplest problem type is univariate; that is, a single variable.
		The universal power and importance of the univariate model is that it can easily be extended to the more general case where the deterministic component is not just a constant, but is in fact a function of many variables, and the engineering objective is to characterize and model the function.
		Thus if the residuals from the fitted model do in fact behave like the ideal, then testing of underlying assumptions becomes a tool for the validation and quality of fit of the chosen model.
	www.itl.nist.gov /div898/handbook/eda/section2/eda21.htm (286 words)

	CLN, a Class Library for Numbers: Univariate polynomials
		CLN implements univariate polynomials (polynomials in one variable) over an arbitrary ring.
		Univariate polynomial rings are constructed using the functions
		Compares two univariate polynomials, belonging to the same univariate polynomial ring, for equality.
	www.ginac.de /CLN/cln_9.html (500 words)

	UNIVARIATE SURVEY DATA ANALYSIS
		Univariate analysis is the examination of the distribution of cases on only one variable at a time.
		Whereas univariate analyses serve the purpose of describing the survey sample and making inferences about the population from which the sample was selected, bivariate and multivariate analyses in the social sciences are aimed primarily at explaining social phenomena.
		While univariate analysis looks at variables one at a time, bivariate analysis focuses on the relationship between two variables.
	www2.chass.ncsu.edu /mlvasu/ps471/D8.htm (3713 words)

	R: Tests for Univariate and Multivariate Balance
		Univariate balance tests will be conducted for each of the regressors included in this model.
		If the ks option is set to true, the univariate KS test is calculated for all non-dichotomous variables.
		The bootstrap-Monte Carlo version of the KS test is highly recommended because the usual KS test is not consistent when there are point masses in the distributions being compared, and the bootstrap is needed because parameters are being estimated in the logit model.
	sekhon.berkeley.edu /matching/MatchBalance.html (945 words)

	Univariate and Multivariate Power Analysis
		Estimating power in the multivariate case is considerably more difficult than estimating power in the univariate case, mainly because the estimates of effect size and measures of strength of association are more complicated and more difficult to obtain.
		As in univariate analysis, several hypotheses are possible, including a test of the full model regression, a test of any partialled subset of of one or more predictor variables, or more complex hypothesis tests.
		If the univariate tests of are of equal theoretical importance, their pfowers should be estimated separately.
	www.albany.edu /faculty/rfh64/classes/ecpy820/power.html (4106 words)

	EDA Short Course: Part 1: Examining Univariate Distributions
		The stem and leaf display is a form of histogram that retains the actual data values while showing the shape of their distribution.
		PROC UNIVARIATE in SAS constructs stem and leaf displays for each variable when you use the PLOT option.
		Figure 1 shows the stem and leaf portion of the PROC UNIVARIATE output for the infant mortality data.
	www.math.yorku.ca /SCS/Courses/eda/eda1.html (3865 words)

	Proc Univariate for Analyzing Data
		Proc UNIVARIATE requires that the input data be in tab separated value (tsv) format.
		The primary use of proc UNIVARIATE is to determine the characteristics of a population in order to determine what further testing and analysis is required.
		Analysts interested in Proc Univariate may also be interested in Proc Means, Proc Outlier, Proc Sort and Proc Benford.
	www.ezrstats.com /EZSW/p23.htm (128 words)

	BioMed Central \| Full text \| A comparison of univariate and multivariate gene selection techniques for classification ...
		In contrast to the univariate approaches, which define the search path through the space of gene sets based on the univariate evaluation of genes, multivariate approaches define the search path based on the informativeness of a group of genes.
		In addition, univariate approaches are the best but not significantly better for the Breast Cancer and CNS datasets, and comparable to the best approach in the remaining two cases (Leukemia and Prostate).
		In the univariate approach (U) the informativeness of each gene is evaluated individually, according to a criterion, such as the Pearson correlation, t-statistic or signal-to-noise ratio (SNR) [4,6,11,13].
	www.biomedcentral.com /1471-2105/7/235 (6086 words)

	[ref] 64 Polynomials and Rational Functions
		Therefore two univariate polynomials may be considered to be in the same univariate polynomial ring if their indeterminates have the same number or one if of them is constant.
		A Laurent polynomial is a univariate rational function whose denominator is a monomial.
		The addition and multiplication of univariate polynomials extends to Laurent polynomials (though it might be impossible to interpret a Laurent polynomial as a function) and many functions for univariate polynomials extend to Laurent polynomials (or extended versions for Laurent polynomials exist).
	www-groups.dcs.st-and.ac.uk /~gap/Manuals/doc/htm/ref/CHAP064.htm (5061 words)

	Univariate and Bivariate Tables (TABLES)
		The main use of TABLES is to obtain printed univariate or bivariate frequency tables with optional row, column and corner percentages and optional univariate and bivariate statistics.
		Both univariate/bivariate tables and bivariate statistics can be output to a file so that can be used with a report generating program for being printed in a user designed format, or can be input to GraphID or other packages such as EXCEL for graphical display.
		These various items may be printed in a single table with a possible six items per cell, or each may be printed as a distinct table.
	www.unesco.org /webworld/idams/Doc/ManualHtml/E1tables.htm (3587 words)

	The UNIVARIATE Procedure : Results
		Each analysis variable is treated individually; a missing value for an observation in one variable does not affect the calculations for other variables.
		PROC UNIVARIATE tabulates the number of the missing values and reports this information in the procedure output.
		By default, PROC UNIVARIATE displays a table that lists observed and estimated quantiles for the 1, 5, 10, 25, 50, 75, 90, 95, and 99 percent of a fitted parametric distribution.
	www.asu.edu /it/fyi/dst/helpdocs/statistics/sas/sasdoc/sashtml/proc/z10ults.htm (826 words)

	The UNIVARIATE Procedure : PROC UNIVARIATE Statement
		By default, PROC UNIVARIATE treats observations with negative weights like those with zero weights and counts them in the total number of observations.
		If value is a proportion p between 0 and.5, the number of observations that PROC UNIVARIATE trims is the smallest integer that is greater than or equal to np, where n is the number of observations.
		If value is a proportion p between 0 and.5, the number of observations that PROC UNIVARIATE uses is equal to the smallest integer that is greater than or equal to np, where n is the number of observations.
	www.okstate.edu /sas/v7/sashtml/books/pguide/z0146803.htm (1601 words)

	Ex. 2 Univariate descriptive plots (Site not responding. Last check: 2007-11-01)
		The objective of this exercise is to demonstrate some of the basic univariate plots for displaying data.
		The univariate "scatter diagram" is a very simple plot of the values of a variable, plotted vs the observation number, or row number in a rectangular data set (labeled "Index" on the plot).
		Compare it to the univariate scatter diagram and strip plot for Length.
	geography.uoregon.edu /bartlein/courses/geog414s06/exercises/ex2.htm (1248 words)

	Univariate Statistics II (Site not responding. Last check: 2007-11-01)
		Psyc611 is the second in a sequence of univariate statistics taught through the Department of Psychology.
		It is designed primarily to meet the needs of graduate students who already have some background in univariate statistic techniques.
		Advanced undergraduates with an appropriate background in statistics (i.e., normally at least an A- in Psyc209/212 or equivalent background) and who plan to undertake graduate work in psychology also may find Psyc611 suited to their needs.
	www.tulane.edu /~PsycStat/ruscher/Psyc611/Psyc611.htm (180 words)

	The UNIVARIATE Procedure : PROC UNIVARIATE Statement (Site not responding. Last check: 2007-11-01)
		PROC UNIVARIATE adds the features in this data set to every high-resolution graph that is produced in the PROC step.
		PROC UNIVARIATE does not use the ANNOTATE= data set unless you create a high-resolution graph with the HISTOGRAM, PROBPLOT, or QQPLOT statement.
		If you omit the libref, PROC UNIVARIATE looks for the catalog in the temporary library called WORK and creates the catalog if it does not exist.
	netra.msu.montana.edu /docs/sas/proc/z0146803.htm (1935 words)

	univariate - OneLook Dictionary Search
		Tip: Click on the first link on a line below to go directly to a page where "univariate" is defined.
		univariate : Dorland's Illustrated Medical Dictionary [home, info]
		Phrases that include univariate: univariate design, univariate regression model
	www.onelook.com /?w=univariate (111 words)

	A simple factorization algorithm for univariate polynomials
		A simple factorization algorithm is developed for univariate polynomials.
		The algorithm was named the binary factoring algorithm since it determines factors to a polynomial modulo 2^n for successive values of n, effectively adding one binary digit to the solution in each iteration.
		Only univariate polynomials with rational coefficients have been considered so far.
	homepage.mac.com /yacas/manual/Algochapter3.html (2607 words)

	Weighted Medians with Proc Univariate
		In SAS, use of the weight command by itself will weight the mean in a PROC UNIVARIATE but not the median and other quantiles.
		The advantage to this approach is more efficient print outs than the PROC FREQ with WEIGHT, because PROC UNIVARIATE produces a single page of output.
		The disadvantage is that the PROC UNIVARIATE with FREQ {weight} recognizes only the integer portion of the weight variable.
	www.hec.ohio-state.edu /scf/procuniv.htm (165 words)

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