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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.
Examples of grouped, simple and frequency distribution data
Of the 6,325 kindergarten students who participated in the study, almost half or 3,052 were eligible for a free lunch program. The categorical variable sesk (1 == free lunch, 2 = n
A salesperson visits from house to house to sell her knives. The probability that she makes a sale at a random house is .3. Given that she makes a sale, the sale is worth $100 with
Assume that a simple random sample has been selected from a normally distribute population and test the given claim. Identify the null and alternative hypotheses, test statistic,
The project of building a backyard swimming pool consists of eight major activities and has to be completed within 19 weeks. The activities and related data are given in the follow
In a management class of 100 childerns' 3 languages are offered as an additional subject viz. Hindi, English and Kannada. There are 28 childrens taking Hindi, 26 taking Hindi and 1
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
The Elementary Teachers' Federation of Ontario make the following claim on their website as of February 13, 2013: For years, the Elementary Teachers' Federation of Ontario (ETFO
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This question explores the effect of estimation error on apparent arbitrage opportunities in a controlled simulation setting. We simulate returns for N = 10 assets over T = 30 year
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