Discuss: Statistical Variation and Regression Methods
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1.What are the implications of statistical variation? Why are we interested in understanding and measuring variation?
Besides using variation in the world of quality, there are also social implications. For example, what does statistical variation suggest about how we ought to judge and treat ourselves and others? Cite an example of these implications from your own experience.
2. Linear Regression is a useful method if you want to be able to predict one variable based on knowledge of another. Discuss other prediction methods that might be useful. Also, discuss some of the more advance Regression methods. When would these methods be appropriate? Provide examples of each method.
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Solution Summary
In about 370 words, this solution discusses the importance of variation to statistics, a factor which seems to be often ignored. Furthermore, this response also details different prediction methods in addition to linear regressions, starting with the more common methods and progressing to the more advanced techniques.
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1. Statistical variation is variability or spread in a variable or a probability distribution. Common examples of statistical variation are variance, standard deviation, and interquartile range. Though statistical variation can be compared to mathematics, it is the focus on variation that gives statistics the particular content that sets it apart from mathematics. Core elements of statistical analysis are:
- The omnipresence of variation in processes
- The need for data about processes
- The quantification of variation
- The explanation of variation
It is the recognition of this variation which is critical ...
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