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What is the difference between parametric and non parametric?
Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution. Nonparametric tests require less restrictive assumptions about the data than parametric restrictions. We can perform the analysis of categorical and rank data using nonparametric tests.
Distingnish between parametric and nonparametric statistics. Why the parametric statistics are considered more powerful than the nonparametric statistics. Explain.?
Parametric statistical tests assume that the data belong to some type of probability distribution. The normal distribution is probably the most common. That is, when graphed, the data follow a "bell shaped curve".On the other hand, non-parametric statistical tests are often called distribution free tests since don't make any assumptions about the distribution of data. They are often used in place of parametric tests when one feels that the assumptions of the have been violated such as skewed data.For each parametric statistical test, there is one or more nonparametric tests. A one sample t-test allows us to test whether a sample mean (from a normally distributed interval variable) significantly differs from a hypothesized value. The nonparametric analog uses the One sample sign test In one sample sign test,we can compare the sample values to the a hypothesized median (not a mean). In other words we are testing a population median against a hypothesized value k. We set up the hypothesis so that + and - signs are the values of random variables having equal size. A data value is given a plus if it is greater than the hypothesized mean, a negative if it is less, and a zero if it is equal.he sign test for a population median can be left tailed, right tailed, or two tailed. The null and alternative hypothesis for each type of test will be one of the following:Left tailed test: H0: median ≥ k and H1: median < kRight tailed test: H0: median ≤ k and H1: median > kTwo tailed test: H0: median ≠ k and H1: median = kTo use the sign test, first compare each entry in the sample to the hypothesized median k.If the entry is below the median, assign it a - sign.If the entry is above the median, assign it a + sign.If the entry is equal to the median, assign it a 0.Then compare the number of + and - signs. The 0′s are ignored.If there is a large difference in the number of + and - signs, then it is likely that the median is different from the hypothesized value and the null hypothesis should be rejected.When using the sign test, the sample size n is the total number of + and - signs.If the sample size > 25, we use the standard normal distribution to find the critical values and we find the test statistic by plugging n and x into a formula that can be found on the link.When n ≤ 25, we find the test statistic x, by using the smaller number of + or - .So if we had 10 +'s and 5 -'s, the test statistic x would be 5. The zeros are ignored.I will provided a link to some nonparametric test that goes into more detail. The information about the Sign Test was just given as an example of one of the simplest nonparametric test so one can see how these tests work The Wilcoxon Rank Sum Test, The Mann-Whitney U test and the Kruskal-Wallis Test are a few more common nonparametric tests. Most statistics books will give you a list of the pros and cons of parametric vs noparametric tests.
Is sonic stronger than tails?
sonic is stronger than tails.
Why are parametric eqautions bad?
In mathematics, parametric equations of a curve express the coordinates of the points of the curve as functions of a variable, called a parameter.[1][2] For example,are parametric equations for the unit circle, where t is the parameter. Together, these equations are called a parametric representation of the curve.A common example occurs in kinematics, where the trajectory of a point is usually represented by a parametric equation with time as the parameter.The notion of parametric equation has been generalized to surfaces, manifolds and algebraic varieties of higher dimension, with the number of parameters being equal to the dimension of the manifold or variety, and the number of equations being equal to the dimension of the space in which the manifold or variety is considered (for curves the dimension is one and one parameter is used, for surfaces dimension two and two parameters, etc.).The parameter typically is designated t because often the parametric equations represent a physical process in time. However, the parameter may represent some other physical quantity such as a geometric variable, or may merely be selected arbitrarily for convenience. Moreover, more than one set of parametric equations may specify the same curve.
Why is a square stronger than a triangle?
It is NOT!
