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As we stated above, we start factor analysis with principal component analysis, but we quickly diverge as we apply the a priori knowledge we brought to the problem.
This knowledge may be of the form that we "know" how inany factors there should be or it may be more of the nature that allows our experience and intuition about the data guide us as to how many factors there should be. As before, with PCA, we can take the eigenvectors (of unit length) and weight them with the square root of the corresponding eigenvalue:
Consider the sample of 60 package design ratings given in the table below. A Sample of Package Design Ratings (Composite S
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
Differentiate between prediction, projection and forecasting.
Explanation of descriptive statistics Describe what these descriptive statistics show or what recommendations you would create to AIU. What information do you now have as a re
Celia is a nurse in a geriatric ward. She noticed that older persons in her care are having problems sleeping at night. She decided to introduce non-pharmocologic ways of relaxat
Where do I Access the gss04student_corrected dataset
Weighted Arithmetic Mean Another aspect to be considered is the importance we assign to each observation. The arithmetic mean as we calculated it so far gives equal
Is the random vector (Trunk Space, Length, Turning diameter) of US car normally distributed? Why? If yes, find the unbiased estimators for the mean and variance matrix of (Trunk Sp
Large Sample Test for Mean A random sample of size n (n > 30) has a sample mean . To test the hypothesis that the population mean μ has a specified value μ 0 let us formu
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