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The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the multinomial distribution. The continuous variables are supposed to have a multivariate normal distribution in which the means of the variables are permitted to vary from cell to cell of the contingency table, but with the variance-covariance matrix of variables being common to all cells. When there is the single categorical variable with two categories the model becomes that supposed by Fisher's linear discriminant analysis.
1) Consider an antenna with a pattern: G(θ,φ) = sinn(θ/θ0) cos(θ/θ0) where θ0 = Π/1.5 (a) What is the 3-dB bandwidth? (b) What is the 10-dB beam width? (c) What is t
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
Link functions: The link function relates the linear predictor ηi to the expected value of the data. In classical linear models the mean and the linear predictor are identical
we are testing : Ho: µ=40 versus Ha: µ>40 (a= 0.01) Suppose that the test statistic is z0=2.75 based on a sample size of n=25. Assume that data are normal with mean mu and standa
In a mathematics examination the average grade was 82 and the standard deviation was 5. all students with grade from 88 to 94 received grade of B. if the grade are approximately no
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
Auto correlation : The correlation of the internal observations in the time series, generally expressed as a function of the time lag between the observations. It is also used for
The probability distribution, f (x), of largest extreme can be given as The location parameter, α is the mode and β is the scale parameter. The mean, variance skewn
A comprehensive regression analysis of the case study London has been carried out to test the 4 assumptions of regression: 1. Variables are normally distributed 2. Linear rel
Bimodal distribution : The probability distribution, or we can simply say the frequency distribution, with two modes. Figure 15 shows the example of each of them
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