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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
The Null Hypothesis - H0: There is no autocorrelation The Alternative Hypothesis - H1: There is at least first order autocorrelation Rejection Criteria: Reject H0 if LBQ1 >
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ain why the simulated result doesn''t have to be exact as the theoretical calculation
Matching coefficient is a similarity coefficient for data consisting of the number of binary variables which is often used in cluster analysis. It can be given as follows he
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A manufacturing company has two factories F 1 and F 2 producing a certain commodity that is required at three retail outlets M 1 , M 2 and M 3 . Once produced, the commodity is
A study not involving the passing of time. All information is collected at the same time and subjects are contacted only once. Many surveys are of this type. The temporal sequence
Suppose we estimate the following model: Passengersi = 1 + 2Populationi + ui a) Generate a scatter plot with passengers on the vertical axis and population on the horizonta
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