
	Where results make sense

Topic: Statistical inference

Related Topics

Statistical theory

Statistics

Testing statistical hypotheses

Statistical model

Frequency probability

Statistical assumptions

Posterior probability distribution

Statistical population

Likelihood principle

Statistical significance

Bayesian analysis

	Statistical Inference - Overview: Imaging Research Inc.
		The role of statistical analysis is selection of a small number of meaningful observations from among the many that constitute an array study.
		Statistical inference techniques differ from data mining methods in that they provide tests of specific statistical hypotheses and their associated probability values.
		Statistical inference procedures may be used when there are a priori experimental hypotheses (e.g., "cell line A expresses gene X at a level different from cell line B") and can also be used to perform "shotgun" testing.
	www.imagingresearch.com /products/AST_details.asp (1542 words)

	Statistical Inference (via CobWeb/3.1 planetlab2.cs.umd.edu) (Site not responding. Last check: 2007-10-14)
		Statistical inference concerns the problem of inferring properties of an unknown distribution from data generated by that distribution.
		The most common type of inference involves approximating the unknown distribution by choosing a distribution from a restricted family of distributions.
		A sufficient statistic summarizes all of the information in a random sample so that knowledge of the individual values in the sample is irrelevant in searching for a good esimator for theta.
	www.cs.brown.edu.cob-web.org:8888 /research/ai/dynamics/tutorial/Documents/StatisticalInference.html (792 words)

	Recommendations for Teaching the Reasoning of Statistical Inference
		Moreover, instructors of introductory statistics often treat statistical inference as an isolated subject with little connection to the issues of exploratory data analysis and data collection that precede it in most courses.
		While statistical inference is a widely used and very important class of techniques, it is just one component of a statistical analysis.
		Statistical education reform emphasizes active learning on the part of students, conceptual understanding of fundamental statistical ideas, use of engaging applications involving genuine data, and development of student communication skills.
	www.rossmanchance.com /papers/topten.html (3703 words)

	RedOrbit - Science - Statistical Inference and Aristotle's Rhetoric (Site not responding. Last check: 2007-10-14)
		It is proposed that statistical tests work in the same way-in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely.
		For example, statistical tests of differences between groups are less to be trusted where the shapes of the distributions of the data in two groups are not identical, and this is especially so if the number in one of the groups is small.
		Statistical tests have no impact where the effect under test is not a sign that the effect has not arisen by chance.
	www.redorbit.com /news/display?id=109737 (4719 words)

	Statistical Inference, Part 2
		The manufacturing representative from the supplier of machine X contends that the result is a mere "statistical fluke." Assuming that a "statistical fluke" is something that has less than 1 chance in 100, test the hypothesis that both variances are actually equal.
		The test statistic used to test for equality of two sample variances is the F statistic, which, for this example, is given by the equation
		The new methods are conceptually quite simple: using the data from a sample, calculate the statistic of interest repeatedly and examine the distribution of the statistic.
	www.qualityamerica.com /knowledgecente/articles/CQE_IIICb.html (1360 words)

	Standard 5-1 : NCES Statistical Standards
		Statistical analysis techniques must be used that are appropriate for the specific research question.
		The efficacy of individual statistical approaches depends on the assumptions of the techniques having been met; therefore, the assumptions underlying the techniques must be discussed.
		If a statistically significant difference for a total group under study is observed, but similar subgroup differences of the same magnitude are associated with smaller sample sizes and/or larger standard errors and are not statistically significant, this may be noted.
	nces.ed.gov /statprog/2002/std5_1.asp (1342 words)

	Statistical Inference Package: Application-Oriented Statistical Inference
		Statistical Inference Package provides many sophisticated statistical regression models, such as univariate/multivariate-linear/nonlinear, logistic, Poisson, multinomial, etc. It uses a new method for calculating profile-likelihood-based confidence intervals for general parametric functions in general parametric statistical models.
		Esa Uusipaikka is a professor of statistics at the University of Turku in Finland, from which he also received his Ph.D. He has published several articles on statistical inference.
		Statistical Inference Package requires Mathematica 5.0 or higher and is available for Windows, Mac OS X, and Linux.
	www.wolfram.com /products/applications/sip (160 words)

	1 Statistical inference, estimation, hypothesis testing (via CobWeb/3.1 planetlab2.cs.umd.edu) (Site not responding. Last check: 2007-10-14)
		Its aim is to enable inferences to be drawn about the model from the information provided by the sample data or to construct procedures to aid the taking of decisions relevant to the practical situation.
		Statistical decision-making increases the reliability of the research, because we can generalise the results of our data to the larger population.
		If we are attempting to make an inference about a normal distribution on the basis of one random sample, we shall know the sample size, n, but not the population standard deviation,.
	www.uku.fi.cob-web.org:8888 /~mauranen/advbis/advbis1.htm (1189 words)

	Statistical model - Wikipedia, the free encyclopedia
		Observations of a variety of statistical attributes is a common way of studying relationships among the attributes of a single unit.
		We may really be interested in a statistical population (or set) of similar units rather than in any statistical unit per se.
		Statistical inference is the art of making statements about which elements of this set are likely to be the true one.
	en.wikipedia.org /wiki/Statistical_model (263 words)

	Statistical Inference
		The subject of statistical inference is large and it is covered in many different books on introductory statistics.
		These sample statistics are called point estimators because they are single values used to represent population parameters.
		It is also possible to construct an interval about the statistics that has a predetermined probability of including the true population parameter.
	www.qualityamerica.com /knowledgecente/articles/CQE_IIIC.html (798 words)

	Statistical inference - Wikipedia, the free encyclopedia
		It has been suggested that this article or section be merged with inferential statistics.
		Inferential statistics or statistical induction comprises the use of statistics to make inferences concerning some unknown aspect (usually a parameter) of a population.
		Statistical inference is inference about a population from a random sample drawn from it or, more generally, about a random process from its observed behavior during a finite period of time.
	en.wikipedia.org /wiki/Statistical_inference (162 words)

	Inferential Statistics
		In estimation, a sample from a population is studied and an inference is made about the population based on the sample.
		The "power" of a statistical test refers to the probability of claiming that there is a significant difference when this is true.
		A typical example might be comparing a variable in two experimental groups of patients, one treated with drug A and one treated with drug B. Such situations are common in medicine where an accepted treatment already exists and it would not be ethical to withhold this from a control group.
	www-micro.msb.le.ac.uk /1010/1011-20.html (3379 words)

	ST372: Introduction Statistical Inference & Regression
		The objective of this course, ST372, is to develop a calculus-level understanding and working knowledge of statistical inference.
		The course provides an introduction to the rudiments of statistical inference based on point and interval estimation of population parameters and hypothesis testing including the use of t, chi-square and F. Simple linear regression and correlation analysis will also be introduced.
		Two sample inference: tests and confidence intervals for a difference between two population means, population proportions and population variances.
	www.stat.ncsu.edu /people/ghosh/st372 (709 words)

	Statistical Literacy Milo Schield
		Statistical literacy is critical thinking about statistics as evidence for inferences.
		Statistical literacy should give students the ability to evaluate the strength of statistics as evidence in arguments about causation.
		Statistical Literacy and Evidential Statistics, 1998 JSM ASA
	www.augsburg.edu /ppages/~schield (688 words)

	REGISTER OF PACKAGE PROGRAMS FOR EXACT STATISTICAL INFERENCE (Site not responding. Last check: 2007-10-14)
		All statistical tests of SCRT are based on the principle of randomization, and are implementations of the procedures described in journals like Behavioral Assessment and Behaviour Research and Therapy, and in the handbook on randomization tests by Eugene Edgington.
		Therefore, in SCRT a Test Statistics Editor is provided where customized statistics may be built and selected as the criterion for the randomization test.
		Besides statistical tests, also descriptive statistics, graphical analyses, plots of the randomization distribution, and plots of the evolution of the p-value during the analysis can easily be obtained.
	ces.iisc.ernet.in /hpg/envis/regdoc722.html (3231 words)

	ASYMPTOTIC THEORY OF QUANTUM STATISTICAL INFERENCE (Site not responding. Last check: 2007-10-14)
		Quantum statistical inference, a research field with deep roots in the foundations of both quantum physics and mathematical statistics, has made remarkable progress since 1990.
		It focuses on the asymptotic theory, which is one of the central issues of mathematical statistics and had not been investigated in quantum statistical inference until the early 1980s.
		The reader is expected to have only elementary mathematical knowledge, and therefore much of the content will be accessible to graduate students as well as research workers in related fields.
	www.worldscibooks.com /physics/5630.html (245 words)

	Principles of Statistical Inference - Cambridge University Press
		Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field.
		The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed.
		The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.
	www.cambridge.org /uk/catalogue/catalogue.asp?isbn=0521685672 (270 words)

	Statistical Inference to Legal Argumentation (Site not responding. Last check: 2007-10-14)
		In a case where expert witnesses will be brought in to talk about scientific facts, an experienced lawyer will ask his opponents to produce all statistical evidence the expert witness has used to draw preliminary conclusions, an explanation of the methods used to arrive at inferences, and references to academic literature supporting all underlying postulates.
		Perhaps no other cases in recent memory have made the public more aware of the use of statistical evidence than (1) the O.J. Simpson murder trial (in which he was acquitted of criminal charges) and (2) the civil trial which followed (in which he was found responsible for the wrongful death of the murder victims).
		Andrews predicated his statistics on a study which was allegedly conducted in Canada among subjects, all of whom were Caucasian.
	technologynotebook.com /features/experwits.html (3098 words)

	Bayesian Statistical Inference
		The following JavaScript performs the Bayesian inference by combining the sample information with prior information to estimate the mean of a normal population.
		Notice that before using this JavaScript it is necessary to perform the Test for Normality.
		This is expressed in terms of a probability distribution known as the prior distribution.
	home.ubalt.edu /ntsbarsh/Business-stat/otherapplets/BayesInfer.htm (352 words)

	Gary King - Software
		Zelig: Everyone's Statistical Software, by Kosuke Imai, Gary King, and Olivia Lau, is a complete statistical package and framework for statistical inference that can estimate, help interpret, and present the results from, a large range of statistical methods.
		If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use.
		This implements statistical procedures described in a series of journal articles and has been used during redistricting in many states by judges, partisans, governments, private citizens, and many others.
	gking.harvard.edu /stats.shtml (1086 words)

	Essentials of Statistical Inference - Cambridge University Press
		This engaging textbook presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them.
		It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject.
		Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential.
	www.cambridge.org /uk/catalogue/catalogue.asp?isbn=0521839718 (269 words)

	Task Force on Statistical Inference Initial Report (DRAFT) (Site not responding. Last check: 2007-10-14)
		This report is the result of the initial 2-day meeting of the Task Force on Statistical Inference held December 14-15, 1996 at the Newark Airport.
		It is the view of the task force that there aremany ways of using statistical methods to help us understand the phenomena we are studying (e.g., Bayesian methods, graphical and exploratory data analysis methods, hypothesis testing strategies).
		We endorse a policy of inclusiveness that allows any procedure that appropriately sheds light on the phenomenon of interest to be included in the arsenal of the research scientist.
	www.apa.org /science/tfsi.html (963 words)

	Amazon.com: Statistical Inference: Books: George Casella,Roger L. Berger (Site not responding. Last check: 2007-10-14)
		Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts.
		Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background.
		It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
	www.amazon.com /Statistical-Inference-George-Casella/dp/0534243126 (1491 words)

	MIT OpenCourseWare \| Mathematics \| 18.441 Statistical Inference, Spring 2002 \| Home
		The "leverage" quantifies the potential influence of a "response variable" on the slope of the least-squares line, as a function of a "predictor variable". It is the square of the correlation between the response variable and the slope when the predictor variable is held constant. (Image by Dr. Michael Hardy.)
		This undergraduate course in statistical inference features lecture notes, assignments, exams, and study materials.
		More theoretical than 18.443 (Statistics for Applications) and more detailed in its treatment of statistics than 18.05 (Introduction to Probability and Statistics).
	ocw.mit.edu /OcwWeb/Mathematics/18-441Statistical-InferenceSpring2002/CourseHome/index.htm (116 words)

	BIPS: Bayesian Inference for the Physical Sciences
		Genz is a leader in the development of new algorithms for numerical computation of mulitiple integrals; his recent research focuses on integrals that arise in Bayesian inference.
		BUGS is a program for Bayesian inference using the Gibbs Sampler Markov chain Monte Carlo technique produced by the Biostatistics Unit of the Medical Research Council of the United Kingdom.
		This annual workshop, hosted by the Statistics Department at Carnegie-Mellon University, offers a variety of lecture and poster presentations, with special in-depth lectures and discussion of a few chosen case studies of practical applications of Bayesian inference.
	www.astro.cornell.edu /staff/loredo/bayes (2872 words)

	Statistical Hypothesis Inference
		Rosnow, R. L., and Rosenthal, R. Statistical procedures and the justification of knowledge in psychological science.
		Schmidt, F. Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers.
		Statistical methods in psychology journals: Guidelines and explanations.
	core.ecu.edu /psyc/wuenschk/StatHelp/NHST-SHIT.htm (351 words)

	EPA Statistical Primer - Statistical Inference
		Description: When the entire population includes too many items to measure them all, we must sample.
		If we sample randomly, we can apply our conclusions to the entire population using statistical inference.
		Green indicates good condition, yellow indicates moderate, red indicates poor and gray means insufficient data.
	www.epa.gov /bioiweb1/statprimer/inferenc.html (297 words)

	Statistical Inference Package -- from Mathematica Information Center
		Statistical Inference Package is a Mathematica application package that makes it easy for users to do classical likelihood-based statistical inference.
		Many sophisticated statistical regression models such as univariate/multivariate-linear/nonlinear, logistic, Poisson, multinomial, etc.
		Statistical Inference Package: One-Way Frequency Table Example [in Technical Notes]
	library.wolfram.com /infocenter/TechNotes/6026 (144 words)

Try your search on: Qwika (all wikis)