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Correspondence Analysis (CA) is a generalization of PCA to contingency tables. The factors of correspondence analysis give an orthogonal decomposi:ion of the Chi- square associated to the table. In correspondence aria!ysis, rows and columns of the table play a symmetric role and can be represznteci in tli~ sarne plot. When several nominal variables are analyzed, correspondence analysis is generalized &'Multiplc Correspondence Analysis (MCA). The ct;rrespondcnce analysis is also .known as dual or optimal scaling or reciprocal averaging.
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Define sampling unit and population for selecting a random sample in every case. a) 100 voters from a constituency b) 20 stocks of National Stock Exchange c) 50 account ho
Estimate a linear probability model: Consider the multiple regression model: y = β 0 +β 1 x 1 +.....+β k x k +u Suppose that assumptions MLR.1-MLR4 hold, but not assump
The following table shows the results of fitting a linear regression model of starting annual salaries on a constant, GPA (4 point scale), and a variable (Metrics =1) indicating wh
Chi-square analysis can be used with both Goodness-of-Fit Tests and with Tests for Independence. There are specific instances when each test should be used based on the information
Complete the multiple regression model using Y and your combined X variables. State the equation. Next, make sure that you evaluate overall model performance with the Anova table
give a elementary example for characterstics of index number
Collect data about the chosen business problem or opportunity at the company. Explain how you obtained a suitable sample of either qualitative or quantitative data. Review data f
Angle Count method The method for estimating the proportion of the area of a forest which is in fact covered by the bases of trees. An observer goes to each of the number of po
The PCA is amongst the oldest of the multivariate statistical methods of data reduction. It is a technique for simplifying a dataset, by reducing multidimensional datasets to lower
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