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Topic: Sample (statistics)

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	PROPHET StatGuide: Descriptive statistics
		Samples from a continuous distribution may not have any repeated data values, so the mode is generally more informative with samples from discrete distributions.
		The sample variance is the the average of the squared deviations of each sample value from the sample mean, except that instead of dividing the sum of the squared deviations by the sample size N, the sum is divided by N-1.
		The sample interquartile range is the difference between the upper (75th percentile) and lower (25th percentile) quartiles of the data sample, which are the upper and lower bounds of the center half of the data values.
	www.basic.northwestern.edu /statguidefiles/desc.html (2409 words)

	PlanetMath: statistic
		This is an example of a statistic whose range is a function space.
		Although mostly real-valued, a statistic can be vector-valued, or even function-valued as we have seen in earlier examples.
		This is version 8 of statistic, born on 2004-10-24, modified 2007-09-20.
	planetmath.org /encyclopedia/SampleVariance.html (382 words)

	Statistics (The Grinder)
		Sample statistics are a special type of statistic that hold aggregate information about a series of long or double sample values; specifically count (number of samples), sum (total of all sample values), and sample variance.
		This statistic is used to define views for evaluation in the console and has no meaning in a worker process.
		By default, test statistics reports are automatically sent to the console and data log when the test proxy call completes, so the script cannot modify the test statistics after the call.
	grinder.sourceforge.net /g3/script-javadoc/net/grinder/script/Statistics.html (1327 words)

	STA-2122: Introduction to Statistics I
		Sample is a subset of units chosen from the defined population with the purpose of making a statistical inference.
		Representative Sample is a sample that reflects the relevant characteristics of the population.
		Sampling survey is a type of statistical study involving a sample of units from the defined population and a questionnaire.
	www.fiu.edu /~gomezra/STA2023-Vocab.htm (1741 words)

	PSY 138: Social Science Reasoning Using Statistics (Site not responding. Last check: )
		The sample is intended to be "representative" of the population.
		A statistic may be obtained from a single measurement, or it may be derived from a set of measurements from the sample.
		Sampling error is the discrepancy or amount of error, that exists between a sample statistic and the corresponding population parameter.
	www.psychology.ilstu.edu /psy138/liu/4_Tutorial.html (782 words)

	Sampling (statistics) - Wikipedia, the free encyclopedia
		Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference.
		The sampling frame must be representative of the population and this is a question outside the scope of statistical theory demanding the judgement of experts in the particular subject matter being studied.
		Mechanical sampling is typically used in sampling solids, liquids and gases, using devices such as grabs, scoops, thief probes, the coliwasa and riffle splitter.
	en.wikipedia.org /wiki/Sampling_(statistics) (2589 words)

	Sample (statistics) - Wikipedia, the free encyclopedia
		In statistics, a sample is that part of a population which is actually observed.
		If statistical inference is to be used, there must be a way of assigning known probabilities of selection to each sample.
		If the probabilities of different samples are all equal, for example, the method is called simple random sampling.
	en.wikipedia.org /wiki/Sample_(statistics) (238 words)

	Sample Statistics, Population Parameters, and the Sampling Distributions of Sample Statistics (Site not responding. Last check: )
		The variance of the sampling distribution is a measure of the efficiency of the statistic, as is the SE of the statistic.
		For sample statistics that are computed using the values of other sample statistics (like deviation scores which are computed using the sample mean), the value of your N observations to make estimates gets diminished.
		In reporting the shape of the sampling distributions of the mean and variance as well as of their standardized values, I was careful to say that the sampling distribution of the mean is normal when Y is normal and that the sampling distribution of the variance is chi-square when Y is normal.
	www.olemiss.edu /courses/psy501/Lectures/Lecture2/Lex2e.htm (4113 words)

	SurfStat.australia
		Statistical inference is the use of probability theory to make inferences about a population from sample data.
		Sample statistic - numerical characteristic of the sample data such as the mean, proportion or variance.
		Estimates of the population parameters obtained from a sample are called sample statistics (or sample estimates).
	www.anu.edu.au /nceph/surfstat/surfstat-home/4-1-1.html (909 words)

	web page for ed 510 - sampling, samples, sample statistics
		The sample is a subset of that population.
		It is essential that the statistical description of a sample be a closely matched to the statistical description of its parent population.
		The process of sampling subjects continues until all categories are filled, and in filling those categories, the resulting size of each group represents the percentages of each group in the population at large.
	muse.widener.edu /~aad0002/510sample.html (1749 words)

	Univariate Statistics - Two Sample Tests
		With the 2 sample t and z, the x variable is nominal scale of measurement and dichotomous (takes on only 1 of 2 possible values).
		The general form of the formula is the same as for the one sample z test: z equals the difference of the test statistic (the mean) and the null hypothesis value divided by the standard deviation of the statistic.
		In the case of two samples, we simply subtract the means (and divide by the standard error of the difference between means) to obtain the test statistic.
	www.uwsp.edu /psych/cw/statmanual/twosample.html (843 words)

	Univariate Statistics - One Sample Tests
		One sample tests, in general, examples of "goodness of fit" tests where we are testing whether our data supports predictions regarding the value of the population mean.
		The concept is most easily explained in the context of one-sample tests; that is, in cases were we are comparing some group to a known population mean.
		Remember that standard deviation is derived from the sample variance, and that the variance is calculated using the sum of squared deviations from the mean of your sample.
	www.uwsp.edu /psych/cw/statmanual/onesample.html (879 words)

	web page for ed 510 - sampling, samples, sample statistics
		The sample is a subset of that population.
		It is essential that the statistical description of a sample be a closely matched to the statistical description of its parent population.
		Sampling errors are estimated to be 1 percent.
	www2.widener.edu /~aad0002/510sample.html (1749 words)

	statistics@Everything2.com
		Statistics is the branch of mathematics that deals with the collection, analysis, and interpretation of data.
		Statistics are numbers or conclusions drawn from data that describe a trait of that data.
		The median is the middle value of the sample, that means that half of the observations in the sample are larger then the median, and half are smaller.
	www.everything2.com /index.pl?node=statistics (2141 words)

	Z Statistics: Two sample
		The Two sample item on the Z Statistics submenu on the Stat menu provides confidence intervals and/or hypothesis tests for the difference in two means from independent samples (first selected minus second selected).
		In the dialog box, select the variables representing the two samples, then supply (in the order selected) the standard deviation values for each population The two sample Z procedure is illustrated in an example using the reading data.
		In this example, the control group is chosen as the first variable, and the treatment group as the second, and the standard deviations are assumed to be 12 for each population.
	www.stat.sc.edu /webstat/version2.0/stat/TwoSampleZ.html (300 words)

	SAS Elementary Statistics Procedures : Statistical Background
		A statistic is to a sample as a parameter is to a population.
		Statistics from a sample can be used to make inferences, or reasonable guesses, about the parameters of a population.
		The purpose of the statistical methods that have been discussed so far is to estimate a population parameter by means of a sample statistic.
	v8doc.sas.com /sashtml/proc/ztatback.htm (4630 words)

	Review of Elementary Statistics
		In other words, if we were to take thousands of random samples from a population and find each of their means, (with replacement, or from an infinite population), and then put these means in a frequency distribution, we would have an empirical sampling distribution of sample means.
		Its probability depends on the level of alpha, the sample size, the degree to which the actual parameter diverges from the hypothesized one, and whether the tests is a "one tailed" or "two tailed" test.
		A statistic is said to be significant when it leads to rejecting the null hypothesis--when it falls in the region of rejection or has a probability of occurring that is less than alpha if the null hypothesis is true.
	bill.psyc.anderson.edu /exdes/ex1.htm (4020 words)

	School of Mathematics:Statistics - Wikibooks, collection of open-content textbooks
		Fundamentals of Probability, Statistics, Experiments and Data This discusses the history and nature of "What are Probability and Statistics?" The nature of data (types of data—discrete versus continuous, categorical, etc.) is discussed, as well as topics of problems in data gathering, unintended outside forces getting confounded with the data, etc.
		Statistics for Experimenters This is mostly a review course, discussing all previous material in the context of real-world scientific research.
		Multivariate Statistics Everything we've done in Statistics 2 is taken to the n-dimension with an extension from random variables to random vectors.
	en.wikibooks.org /wiki/School_of_Mathematics:Statistics (1822 words)

	Statistics lectures
		"Statistics is [the theory and method of analyzing quantitative data obtained from samples of observations in order to study and compare sources of variation of phenomena, to help make decisions to accept or reject hypothesized relations between phenomena, and to aid in] making [reliable] inferences from empirical observations" (Kerlinger, 1986, p.
		If the mean height of people in the sample is 2m, the mean height of people in the population is close to 2m.
		A test statistic is calculated on a population sample, and converted to a p-value.
	dna-view.com /statistics.htm (605 words)

	Sample Chapter for Schell, M.J.: Baseball's All-Time Best Hitters: How Statistics Can Level the Playing Field.
		Simply defined statistics, like batting average (which equals hits divided by at bats), may be fine to make comparisons between ballplayers playing in the same year in the same ballparks against the same pitchers.
		Statistics that combine various hitting events, which may include weighting of the values of singles, doubles, triples, and home runs (and possibly walks, strikeouts, or other batting events) are searching for the best batters.
		The use of statistics is the way that science often judges whether or not an idea that somebody dreams up is supported by the data.
	www.pup.princeton.edu /chapters/i6550.html (2250 words)

	ViSta: The Visual Statistics System
		A sample distribution is an observed distribution of the values that a variable is observed to have for a sample of individuals.
		A sampling distribution is a theoretical distribution of the values that a specified statistic of a sample takes on in all of the possible samples of a specific size that can be made from a given population.
		A sampling distribution of sample means is a theoretical distribution of the values that the mean of a sample takes on in all of the possible samples of a specific size that can be made from a given population.
	forrest.psych.unc.edu /research/vista-frames/help/lecturenotes/lecture06/sampling.html (1177 words)

	Inferential Statistics (Site not responding. Last check: )
		: We sample the population (in a manner to ensure that the sample correctly represents the population).
		By using statistics, we can take a random sample of adults over 18 years of age, measure their average height, and then infer that the average height of the total population is ``close to'' the average height of our sample.
		The goal of inferential statistics is to use sample statistics to make inference about population parameters.
	stat.tamu.edu /stat30x/notes/node10.html (202 words)

	statistics.com: Sample Size
		The power of a study (the study's ability to prove a treatment effect exists) is determined by such factors as the magnitude of the treatment effect, the sample size, alpha (the level of statistical significance required), and (for survival studies) the study duration.
		He is an active member of the statistical advisory groups of the Cochrane and Campbell Collaborations.
		Participants should be familiar with basic statistics (such as the background provided by Basic Concepts in Probability and Statistics, Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data).
	www.statistics.com /ourcourses/samplesize (825 words)

	Sample Size \| Statistically Significant Consulting
		Since sample size is so important in making statistical inferences, your committee naturally wants to be sure that your dissertation research uses an adequate sample size to effectively address your research questions.
		Power Analysis is a statistical procedure which is used to justify the appropriate sample size for testing a given statistical hypothesis.
		When I help you with the statistical considerations for your dissertation proposal, I will perform a power analysis to justify your sample size and I will determine which statistical methods are appropriate for your data analysis section.
	www.statisticallysignificantconsulting.com /SampleSize.htm (323 words)

	Dr. Arsham's Statistics Site
		Statistics is a science of making decisions with respect to the characteristics of a group of persons or objects on the basis of numerical information obtained from a randomly selected sample of the group.
		There are many statistical procedures for determining, on the basis of a sample, whether the true population characteristic belongs to the set of values in the hypothesis or the alternative.
		Sampling is the selection of part of an aggregate or totality known as population, on the basis of which a decision concerning the population is made.
	home.ubalt.edu /ntsbarsh/Business-stat/opre504.htm (15126 words)

	Transact-SQL Cookbook: Chapter 8: Statistics in SQL
		However, if you need to perform a broad and thorough statistical analysis, you may be better off loading an extract of your database data into a specialized statistics package such as SPSS or GNU R. Statistical calculations often yield values with many digits to the right of the decimal point.
		From a statistical point of view, you are testing a representative sample and then extrapolating the results to the oranges remaining in the box.
		Given a sample set of values, you calculate the mean by summing all the values and dividing the result by the sample size.
	www.oreilly.com /catalog/transqlcook/chapter/ch08.html (8163 words)

	Mod2
		Say a sample was drawn from the population and measured on personal spiritual development (using a valid and reliable instrument), and the mean of this measure on this sample was 25.89 with a standard deviation equal to 5.32.
		Using this sample size in the denominator is basing the SE statistic on what would likely be the sampling distribution if the same number of samples were to be drawn from a population as the number of individual raw scores making up the single data set.
		The sampling process is replicable such that if one selected another sample from the same population one can apply the same estimation procedure to estimate m for the first sample mean.
	guweb2.gonzaga.edu /doctoral/ld722/ld722-2/m2pm.html (3399 words)

	OSU Statistics: Statistics Courses
		Introduction to the basic concepts of probability and statistics; sample statistics, discrete and continuous probability distributions; confidence intervals, and estimation.
		Sampling from finite populations, simple random, stratified, systematic, and cluster sampling designs, ratio and regression estimates; non-sampling errors.
		Statistical failure models, graphical and analytic parametric estimation for censored samples, non-parametric survival function estimation, reliability of composite and repairable systems, Bayesian estimation and prediction.
	www.stat.ohio-state.edu /current/courses/stat.html (1914 words)

	How to Design and Evaluate Research in Education \| Sample Statistics
		Graphs and sample statistics for three data sets are presented in this section and the "Sample Graphs" section.
		These data are used to illustrate the graphs and statistics that are commonly employed for a single quantitative variable.
		The data are used to illustrate graphs and statistical analysis of two related quantitative variables.
	highered.mcgraw-hill.com /sites/0072532491/student_view0/sample_statistics.html (160 words)

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