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Investigate the use of fixed and percentile meshes when applying chi squared goodness-of- t hypothesis tests.
Apply the oversmoothing procedure to the LRL data. Compare the results of using the range and variance as measures of scale.
Find an oversmoothed rule based on the interquartile range as a measure of scale.
Use Taylor series and Riemannian integral approximations to verify
Carefully evaluate and verify the formulas in Equation. Hint :
What are the UCV and BCV bin widths for the LRL and snowfall data?
The UCV rule seems to estimate something between ISE and MISE [the middle quantity, , being the focus]. Using the exact MISE formula with Normal and lognormal data, investigate the behavior of the terms in the UCV approximation.
Take a Normal sample of size 1,000. Apply increasing degrees of rounding to the data and compare the resulting UCV curves. At what point does the minimizer of UCV become h = 0?
a) List down several measures of central tendency and define the difference among them? b) What do you mean by confidence interval, and why it is useful? What is a confidence lev
To determine the proportion of people in your town who are smokers, it has been decided to poll people at one of the following local spots: (a) the pool hall; (b) the bowling alley
Question 1 Suppose that you have 150 observations on production (yt) and investment (it), and you have estimated the following ADL(3,2) model: (1 – 0.5L – 0.1L2 – 0.05L3)yt = 0.7
characteristic of latin square design
Risk of Portfolios So far, we have seen the application of standard deviation in the context of risk in single investment. But usually most investors hold portfolios of securi
mle
what is the use of applied statistic in our daily routin life
Bernoulli's Theorem If a trial of an experiment can result in success with probability p and failure with probability q (i.e.1-p) the probability of exactly r success in n tri
Statistical Errors Statistical data are obtained either by measurement or by observation. Hence to think of perfect accuracy is only a delusion or a myth, It is no
find the expected value of the mean square error and of the mean square reggression
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