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Correspondence analysis is an exploratory technique used to analyze simple two-way and multi-way tables containing measures of correspondence between the rows and colulnns of any given data. The results provide information almost similar to those produced by Factor Analysis techniques, and they allow us to explore the structure of categorical variables included in the table. Multiple correspondence analysis (MCA) is an extension of simple correspondence analysis to more than two variables. MCA can be used to analyze several contingency tables by generalizing CA.
Disadvantages For calculating median it is necessary to arrange the data; other averages do not need any arrangement. Since it is a positional average, its value is not d
f(x,y)=c(6-x-y) ,o find P(X+Y
If the test is two-tailed, H1: μ ≠ μ 0 then the test is called two-tailed test and in such a case the critical region lies in both the right and left tails of the sampling distr
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
Sampling A Population is a collection of all the data points being studied. For example, if we are studying the annual incomes of all the people in India, then the population
CERTIFICATE OF AIRWORTHINESS FOR EXPORT When aircraft manufacturers go into series production of a new type of aircraft, then obviously they are hopeful of world wide sales. Sim
Stratified Random Sampling: This method of sampling is used when the population is comprised of natural subdivision of units, The method consist in classifying the population u
Question: (a) A normal distribution is thought to have a mean of 50. A random sample of 100 gave a mean of 52.6 and a standard deviation of 14.5. A significance test was carri
Simple Random Sampling In Simple Random Sampling each possible sample has an equal chance of being selected. Further, each item in the entire population also has an equal chan
Multivariate analysis involves a set of techniques to analyse data sets on more than one variable. Many of these techniques are modern and often involve quite sophisticated use of
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