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Topic: Parametric statistics

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	RESEARCH FORUM--Nonparametric Statistics: Methods for Analyzing Data Not Meeting Assumptions Required for the ...
		Parametric statistics use mean values, standard deviation and variance to estimate differences between measurements that characterize particular populations.
		In statistics, robustness is the degree to which a test can stray from the assumptions before changing the confidence you have in the result of the statistical test you have used.
		As is the case with all statistical tests of differences, the researcher must interpret parametric statistical conclusions based on ordinal data in light of their clinical or practical implications.
	www.oandp.org /jpo/library/1996_03_105.asp (5206 words)

	Courses
		Statistical procedures valid under unrestrictive assumptions; sign test; confidence intervals; efficiency comparisons; signed rank procedures; Walsh sums; point estimators; two sample rank tests; zeros, ties, and other problems of discrete data; order statistics; Winsorized and truncated point estimators and connection with gross error models; permutation procedures; combinatorial problems, and computer applications.
		Statistical function estimation and classification; reproducing kernel machines, support vector machines; high dimensional model selection and estimation; Bayesian, empirical Bayesian interpretation of nonparametric learning methods; log density ANOVA and graphical models; tree ensemble methods including bagging, boosting, and random forest.
		Statistical and approximation theoretic methods of estimating functions and values of functionals from experimental data; experimental design and data analysis problems that arise as problems in approximation theory; convergence theorems; ill-posed inverse problems; Banach and Hilbert space penalty functionals.
	www.wisc.edu /grad/catalog/letsci/statisC.html (2551 words)

	Encyclopedia :: encyclopedia : Non-parametric statistics (Site not responding. Last check: 2007-11-04)
		The branch of statistics known as non-parametric statistics is concerned with non-parametric statistical models and non-parametric statistical tests.
		Nonparametric models differ from parametric models in that the model structure is not specified a priori, but is instead determined from data.
		Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the frequency distributions of the variables being assessed.
	www.hallencyclopedia.com /topic/Non-parametric_statistics.html (169 words)

	M7PM
		In many other cases, however, parametric tests are not as powerful as their nonparametric counterparts when certain of their assumptions (e.g., homogeneity of variances) have been violated.
		Because the chi-dqure statistic of 2.01 is not greater than the chi-square critical value of 5.99, the decision must be to fail to reject the null hypothesis (refer to Picture 7).
		In analyzing descriptive statistics on the data, she finds that the self-importance data are significantly skewed -- there is a positive skewness such that there are a few individuals who have a very high level of self-importance and these scores produced a skewed situation.
	guweb2.gonzaga.edu /doctoral/ld722/ld722-7/M7PM.html (2874 words)

	Non-Parametric Tests
		Like all of the statistical tests discussed up to this point, non-parametric tests are used to investigate the relationship between two or more variables.
		All of the statistical techniques you have learned up to now have made assumptions regarding the data (in particular regarding the population parameters estimated by the data).
		Parametric tests answer research questions by calculating and comparing the means of groups under study on the dependent variable.
	www.soe.usfca.edu /current_students/spss_tutorial/module4_nonparametric.html (3875 words)

	Nonparametric Statistics
		Without going into too much detail, most common statistical techniques such as analysis of variance (and t- tests), regression, etc. assume that the underlying measurements are at least of interval, meaning that equally spaced intervals on the scale can be compared in a meaningful manner (e.g, B minus A is equal to D minus C).
		On the other hand, nonparametric statistics are less statistically powerful (sensitive) than their parametric counterparts, and if it is important to detect even small effects (e.g., is this food additive harmful to people?) one should be very careful in the choice of a test statistic.
		Note that the chi-square statistic computed for two-way frequency tables, also provides a careful measure of a relation between the two (tabulated) variables, and unlike the correlation measures listed below, it can be used for variables that are measured on a simple nominal scale.
	www.statsoft.com /textbook/stnonpar.html (1917 words)

	Introduction
		Parametric statistics allows us to reduce the data to a few parameters which makes it easier to interpret the data.
		Statistics such as the mean and variance describe the pattern of random events and allow us to evaluate the probability of events of interest.
		Order statistics are non-parametric and only rely upon the weak assumption that the data are samples from a continuous distribution.
	mcs.une.edu.au /~stat354/notes/node39.html (459 words)

	Parametric statistics - Wikipedia, the free encyclopedia
		Parametric inferential statistical methods are mathematical procedures for statistical hypothesis testing which assume that the distributions of the variables being assessed belong to known parametrized families of probability distributions.
		For example, analysis of variance assumes that the underlying distributions are normally distributed and that the variances of the distributions being compared are similar.
		While parametric techniques are robust – that is, they often retain considerable power to detect differences or similarities even when these assumptions are violated – some distributions violate the assumptions so markedly that a non-parametric alternative is more likely to detect a difference or similarity.
	en.wikipedia.org /wiki/Parametric_statistics (146 words)

	UNCW CAS: Department of Mathematics and Statistics - Academic Programs
		Business and industry professionals and public administrators who have limited training in statistics but deal with data on a regular basis and would like to improve their working knowledge of data analysis, use more sophisticated techniques than those in current practice, and/or use the acquired knowledge to advance their career.
		Graduate students in other disciplines who perceive the need for statistical knowledge in their future career or are simply interested in a deeper understanding of statistical methodology as it relates to their field.
		Review of case studies involving consulting with clients on statistical design of experiments and analysis of experimental and observational data; consulting on statistical issues with clients on campus through the departmental consulting center; presentation of oral report on consulting experience.
	www.uncwil.edu /math/program-appliedprog.html (1462 words)

	N633 Graduate Nursing Research
		Once a statistical statement of the null hypothesis is made decisions must be made regarding what statistic to use to test the null hypothesis.
		A parametric statistical test specifies certain conditions about the distribution of responses in the population from which the research sample was drawn.
		A non-parametric statistical test is based on a model that specifies only very general conditions and none regarding the specific form of the distribution from which the sample was drawn or the level of measurement required.
	web.indstate.edu /mary/N633/assumptions.html (620 words)

	Resource Materials: Painless Guide to Statistics Bates College
		Parametric analyses are the oldest and most commonly used type of analysis.
		All parametric statistics have three common assumptions that must be met before proceeding.
		Turning to the statistics table, the critical value of c2 for 1 df and a significance level of 0.05 is 3.84.
	abacus.bates.edu /~ganderso/biology/resources/statistics.html (3381 words)

	How to choose a statistical test
		Commonly used parametric tests are listed in the first column of the table and include the t test and analysis of variance.
		You should definitely choose a parametric test if you are sure that your data are sampled from a population that follows a Gaussian distribution (at least approximately).
		When in doubt, some people choose a parametric test (because they aren't sure the Gaussian assumption is violated), and others choose a nonparametric test (because they aren't sure the Gaussian assumption is met).
	www.graphpad.com /www/Book/Choose.htm (1912 words)

	New View of Statistics: Non-parametric Models
		Another approach to getting confidence limits for the outcome statistic with a rank-transformed variable is to calculate a Cohen-type effect size (change in the mean divided by a standard deviation).
		Let me remind you that this outcome statistic is ideal for studies of average subjects in a population, but it's no good for studies of performance of competitive athletes.
		These analyses are simple applications of parametric modeling that belie their intimidating exotic names.
	www.sportsci.org /resource/stats/nonparms.html (1565 words)

	Statistics
		Parametric statistics assume normal distribution and are used with interval or ratio data
		A “chi square” is the statistic that checks the reliability level or “statistical significance” level
		Correlation statistics are also used for prediction or “regression,” not literally predicting, but predicting potential outcomes
	www.nevada.edu /~drums/435files/chapter12.htm (344 words)

	REVIEW: STATXACT 6: Non-parametric statistics without tears
		Long ago, when I was a student, when a computer and its high priests occupied their own air-conditioned building far from the reach of undergraduates, we had two eccentrically flamboyant statistics lecturers.
		They appear in generic statistics packages, though Monte Carlo simulations of known theoretical distributions (courtesy of all that cheap and readily available computing power) have reduced concerns about assumptional errors.
		The other entries (basic statistics; plots; power and sample size) are smaller affairs, there to support the main show.
	www.scientific-computing.com /scwmayjun04review_statxact.html (1656 words)

	Nonparametric Statistics
		Most commonly used statistical techniques are properly called parametric because they involve estimating or testing the value(s) of parameter(s)--usually, population means or proportions.
		For small samples, the statistic is compared to what would result if the data were combined into a single data set and assigned at random to two groups having the same number of observations as the original samples.
		One answer is given by the asymptotic relative efficiency (ARE) which, loosely speaking, describes the ratio of sample sizes required (parametric to nonparametric) for a parametric procedure to have the same ability to reject a null hypothesis as the corresponding nonparametric procedure.
	www.tufts.edu /~gdallal/npar.htm (2914 words)

	SEE-U - Statistical Test Choice Key - Dr. James A. Danoff-Burg - Columbia University
		In general, these statistical tests are less robust than the parametric statistics.
		Parametric Statistics - A general class of statistics that require the normality assumptions to be met.
		The Comparison of Statistical Methods Chart is useful for comparing the efficacy of different statistical analytical methods and is available from the US Environmental Protection Agency.
	www.columbia.edu /itc/cerc/seeu/atlantic/statistics.html (1999 words)

	Parametric Statistics
		When talking to another professor, Martha is proud to report that her class had an average score of 81.
		Define the term statistic and be sure to point out that everything covered in class today was statistics.
		Identify which statistics are misleading and how they are misleading.
	www.shodor.org /succeed/mathcon/statistics.html (849 words)

	Testing paired observations online. Also for matched samples using non-parametric statistics tests
		One possible way to test if the difference between the respondents before and after the intervention is statistically significant is to apply the procedure t-test as implemented in SISA.
		The intervention does not produce statistically significant improvements in the respondents score, according to this method of testing.
		In that case the t-test tests is too conservative, differences between the two sets of paired observations are not declared statistically significant as quickly as it should.
	home.clara.net /sisa/pairwhlp.htm (1832 words)

	Parametric Assumptions
		The second feature of parametric statistics, with which we are all familiar, is a set of assumptions about normality, homogeneity of variance, and independent errors.
		She then proceeds to test that null by asking whether the obtained difference in sample means is likely to arise when the populations have the same means—she has already assumed that they have the same shape and variance.
		The parametric tests are asking if the means are different, while the randomization tests are acting as if the treatments have different effects, and I am not using the word "effect" there in its technical statistical sense.
	www.uvm.edu /~dhowell/StatPages/Resampling/parametric_assumptions.html (632 words)

	Stats: Parametric versus nonparametric tests
		A parametric test, of course, is a test that requires a parametric assumption, such as normality.
		Whether to choose a parametric versus nonparametric test is a matter of judgement.
		If you have already specified the statistical analysis in your protocol, then stick with your protocol unless your data suggests very strongly that a different approach is called for.
	www.childrens-mercy.org /stats/ask/parametric.asp (558 words)

	Theory and Function of Educational Measurement — FED 785
		This course is designed to provide students the understanding of nonparametric statistical methods pertaining to design and analysis in educational research.
		Parametric statistics will be reviewed and parallel nonparametric statistics will be compared to the characteristics and uses of the parametric statistics.
		Apply knowledge of nonparametric statistics by analyzing research problems and making decisions about the appropriate use of nonparametric procedures.
	www.auburn.edu /~rossma1/nonparam.htm (767 words)

	Agilent \| IC-CAP Statistics Package
		The IC-CAP statistics package (available only on HP-UX and Solaris platforms) provides conventional parametric analysis and a patented non-parametric boundary analysis that was developed by Agilent EEsof EDA.
		With parametric analysis, it is easy to identify the best model parameters to track in electrical test or to build models that predict SPICE parameters or independent factors.
		To perform parametric analysis, each parameter distribution is first transformed into a Gaussian distribution.
	eesof.tm.agilent.com /products/85190a_statistics.html (773 words)

	[No title]
		The R square statistic (use adjusted R square) measures amount of variation in Y explained by the X’s.
		Nonparametric Tests (or Distribution free statistics) All of the above except Chi square are called parametric statistics meaning they assume interval scale measurements and that variables are normally distributed in the population.
		These are parametric statistics since they assume interval scale and normality.
	www.msu.edu /course/prr/844/Stat2001.doc (1457 words)

	WINKS Statistics Software for Research Tutorial - Parametirc and Non-parametric Analysis
		This statistic is used to indicate average value of a population or sample.
		If the mean is combined with another common statistic called the standard deviation, then the pair of number tells the research both the central tendency of the group of number and their spread.
		When statistics are calculated under the assumption that the data follow some common distribution such as the normal distribution we call these parametric statistics.
	www.texasoft.com /tutparam.htm (864 words)

	Statistical Consulting Services - Statistical Resources - Statistics Tutorials
		Each lecture covers the basic theoretical and mathematical background of the statistical methods, and step-by-step instructions are given for carrying out the analyses.
		We also discuss the properties of the normal distribution, which is central to most parametric statistical tests.
		We briefly discuss the concepts of the r-distribution, and witness the development of one of the most common and powerful statistical tests used in modern statistics: the correlation coefficient, r.
	www.stats-consult.com /tutorials.html (1068 words)

	NU604: Syllabus: Course Content: Module 7: Quantitative Analysis of Research Data-Inferential Statistics: Analysis of ... (Site not responding. Last check: 2007-11-04)
		At this point I would like to say a word or two about using parametric versus non-parametric testing and the assumptions that you must make when using each particular type of statistic.
		Parametric statistics test hypotheses based on the assumption that the samples come from populations that are normally distributed.
		Therefore, if you are working with two groups and applying a parametric statistical test to estimate the means of the two populations from which the samples were equal or not, you must make sure you have homogeneity, and normal distribution within these two groups.
	jeffline.tju.edu /Education/dl/NU604/syllabus/module7/stats.html (159 words)

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