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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.
Types of business forecasting are generally as follows: 1. Sales and Demand forecasts 2. Porduction forecasts. 3. Cost Forecasts 4. Financi
Let X, Y, and Z refer to the three random variables. It is known that Var(X) = 4, Var(Y) = 9, and Var(Z) = 16. It is further known that E(X) = 1, E(Y) = 2, and E(Z) = 4. Furthermor
Linear Regression Generally, in two mutually related statistical series, the regression analysis based on graphic method. Under graphic method the values of X and Y variable
how many types of ogive are there
The calculations of arithmetic mean may be simple and foolproof, but the application of the result may not be so foolproof. An arithmetic mean may not merely lack
Definition of Central Tendency The central tendency of a variable means a typical value around which other values tend to concentrate which can be measured. Such concentration
the sum of mean and variance ofabinomia distribution of 5 trials is 9/5, find the binomial distribution.
The 4 assumptions of regression: 1. Variables are normally distributed 2. Linear relationship between the independent and dependent variables 3. Homosced
Using Chi Square Test when more than two Rows are Present To understand this, let us consider the contingency table shown below. It gives us the information about the stage
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