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Biplots: It is the multivariate analogue of the scatter plots, which estimates the multivariate distribution of the sample in a few dimensions, typically two and superimpose on these display representations of the variables on which the samples are measured and observed. The relationships amongst the individual sample points can be simply seen and they can also be related to the values of the measurements, thus making such plots useful for giving the graphical explanation of the data, for detecting patterns, and for giving the results found by more formal methods or techniques of analysis. Figure 16 shows the two-dimensional biplot of a set of multivariate data with 34 observations and 10 variables. Also see principal components analysis and corre- spondence analysis.
Randomization tests are the procedures for determining the statistical significance directly from the data with- out recourse to some particular sampling distribution. For instanc
meaning,uses,shortcomings and drawbacks of vital statistics
Bayes factor : A summary of evidence for the modelM1 against the another modelM0 provided by the set of data D, which can be used in the model selection. Given by the ratio of post
An oil company is considering whether or not to bid for an offshore drilling contract. If they bid, the value would be $600m with a 65% chance of gaining the contract. The company
Bioinformatics : Essentially the application of the information theory to biology to deal with the deluge of the information resulting from the advances in molecular biology. The m
i will like to submit my project for you to do on chi-square, ANOVA, and correlation and simple regression. how can we do this?
Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting
Meta-analysis is the collection of techniques whereby the results of two or more independent studies are statistically combined to yield the overall answer to a question of intere
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
The Null Hypothesis - H0: Model does not fit the data i.e. all slopes are equal to zero β 1 =β 2 =...=β k = 0 The Alternative Hypothesis - H1: Model does fit the data i.e. at
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