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Classification and regression tree technique (CART): The alternative to the multiple regression and associated techniques or methods for determining subsets of the explanatory variables most significant for prediction of the response variable. Rather than ?tting the model to the sample data, a tree structure is obtained by dividing the sample recursively into the various of sets, each division being chosen so as to maximize some measure of difference in the response variable in the resulting two sets. The resulting structure often gives us the easier interpretation than a regression equation, as those variables most significant for the prediction can be quickly identi?ed. In addition this approach does not need distributional assumptions and is also more resistant to the effects of the outliers. At each stage the sample is divided on the basis of a variable, xi, according to answers to such questions as 'Is xi c' (univariate split), is ' Paixi c' (which is linear function split) and 'does xi A' (if xi is the categorical variable). A design of the application of this method or technique is shown in the figure 35.
Linked micro map plot is a plot which provides the graphical overview and the details for spatially indexed statistical summaries. The plot shows the spatial patterns and statisti
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if nR2 > MTB >
An approach of using the likelihood as the basis of estimation without the requirement to specify a parametric family for data. Empirical likelihood can be viewed as the example of
The plot of the number of cases of the disease against the time period. A large and sudden increase corresponds to an epidemic. The example of this is shown in the figure drawn bel
Perturbation theory : The theory useful in assessing how well a specific algorithm or the statistical model performs when the observations suffer less random changes. In very commo
Multilevel models are the regression models for the multilevel or clustered data where units i are nested in the clusters j, for example a cross-sectional study where students are
Negative binomial distribution is the probability distribution of number of failures, X, before the kth success in the sequence of Bernoulli trials where the probability of succes
Reasons for screening data Garbage in-garbage out Missing data a. Amount of missing data is less crucial than the pattern of it. If randomly
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if |t | > t = 1.96
Lie factor : A measure suggested by Tufte for judging the honesty of the graphical presentation of data. Which can be calculated as follows The values close to one are desir
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