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The variables resulting from the recoding categorical variables with more than two categories into the sequence of binary variables. Marital status, for instance, if originally labeled 1 for the married, 2 for single and 3 for divorced, widowed or separated, can be rede?ned in the terms of two variables which are given as follows
Variable 1: 1 if single, 0 otherwise;
Variable 2: 1 if the divorced, widowed or separated, 0 otherwise;
For the married person both the new variables would be zero. In common the categorical variable with k categories would be recorded in the terms of k 1 dummy variables. Such recoding is made in use before polychotomous variables are used as the explanatory variables in a regression analysis to avoid the unreasonable supposition with the original numerical codes for the categories, that is the values 1; 2; ... ; k, correspond to the interval scale. This procedure is generally known as dummy coding
Chernoff's faces : A method or technique for representing the multivariate data graphically. Each observation is represented by the computer-created face, the features of which are
It is the art of attempting to exchange something quite small and certain, for something which are large and uncertain. Gambling is big business; in the US, for instance, it is at
Paired samples are the two samples of the observations with the characteristic feature with each of the observation in one sample have only one matching observation in the other s
replacement problem
Observation-driven model is a term generally applied to models for the longitudinal data or time series which introduce within the unit correlation by specifying the conditional
Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application inc
a. Explain the meaning of the word non-orthogonal. b. What condition(s) must exist for non-orthogonality to occur? Be specific.
Chi-squared distribution : It is the probability distribution, f (x), of the random variable de?ned as the sum of squares of the number (v) of independent standard normal variables
The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti
Please help with following problem: : Let’s consider the logistic regression model, which we will refer to as Model 1, given by log(pi / [1-pi]) = 0.25 + 0.32*X1 + 0.70*X2 + 0.
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