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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.
Sampling Error It is the difference between the value of the actual population parameter and the sample statistic. Samples are used to arrive at conclusions regarding the p
The Null Hypothesis - H0: The random errors will be normally distributed The Alternative Hypothesis - H1: The random errors are not normally distributed Reject H0: when P-v
In PCA the eigknvalues must ultimately account for all of the variance. There is no probability,'no hypothesis, no test because strictly speaking PCA is not a statistical procedure
real time applications on graphical representation of o-give curves
Problem : A company supplying electrical products, places a rush order for electric wires. Consignments of wires are to be sent immediately when they are available. Previous
Why are graphs and tables useful when examining data? A researcher is comparing two middle school 7th grade classes. One class at one school has participated in an arts program
Standard Error The measure of reliability of the estimating equation that we have developed is given by standard error of estimate. The standard error of estimate represented b
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
You want to know the thoughts of air travelers in fields such as tickets, comffort, safety, securuty, services and economic growth. You are given a database and 20 questions to ask
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