Question

what are The main factor of numbers formation? what are they called​

Answer 1

Answer:

Factor analysis is a way to condense the data in many variables into a just a few variables. For this reason, it is also sometimes called “dimension reduction.” You can reduce the “dimensions” of your data into one or more “super-variables.” The most common technique is known as Principal Component Analysis (PCA).Factor analysis is useful in:

Condensing variables

Uncovering clusters of responses

Say you ask several questions all driving at different, but closely related, aspects of customer satisfaction:

How satisfied are you with our product?

Would you recommend our product to a friend or family member?

How likely are to you purchase our product in the future

But you only want one variable to represent a customer satisfaction score. One option would be to average the three question responses. Another option would be to create a factor dependent variable. This can be done by running PCA and keeping the first Principal Component (also known as a factor). The advantage of PCA over an average is that it automatically weights each of the variables in the calculation.

Say you have a list questions and you don’t know exactly which responses will move together and which will move differently; for example, purchase barriers of potential customers. The following are possible barriers to purchase:

Price is prohibitive

Overall implementation costs

We can’t reach a consensus in our organisation

Product is not consistent with our business strategy

I need to develop an ROI, but cannot or have not

We are locked into a contract with another product

The product benefits don’t outweigh the cost

We have no reason to switch

Our IT department cannot support your product

We do not have sufficient technical resources

Your product does not have a feature we require

Other (please specify)

Factor analysis can uncover the trends of how these questions will move together. The following are loadings for 3 factors for each of the variables.

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