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Essay / Research Paper Abstract
A 7 page paper answering six general statistics questions, such as “When should you use an ANOVA (analysis of variance) and when should you use linear regression?” and “What are the appropriate univariate, analytical and inferential statistics to use with data measured at the nominal and ordinal levels?” Bibliography lists 4 sources.
Page Count:
7 pages (~225 words per page)
File: CC6_KSstatsQues.rtf
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Unformatted sample text from the term paper:
only! The student must cite as a source in the writing of their own paper, and may not plagiarize any of the material offered in this
work. 1. When should you use an ANOVA (analysis of variance) and when should you use linear regression? The student could approach this
section in the following way: A t-test compares the means of two groups only. ANOVA provides the same kind of comparison.
The difference between them is that the ANOVA is capable of comparing the means of as many groups as the research design calls for using. ANOVA can be used
for more complex comparisons as well, and it is not only an expanded version of the t-test (Electronic Statistics Textbook, 2003). Regression provides
information about the relationship between "several independent or predictor variables and a dependent or criterion variable. For example, a real estate agent might record for each listing the size of
the house (in square feet), the number of bedrooms, the average income in the respective neighborhood according to census data, and a subjective rating of appeal of the house" (Electronic
Statistics Textbook, 2003). After compiling the information, the agent then could test to "see whether and how these measures relate to the price for which a house is sold.
For example, one might learn that the number of bedrooms is a better predictor of the price for which a house sells in a particular neighborhood than how pretty the
house is (subjective rating)" (Electronic Statistics Textbook, 2003). A common use for linear regression is that in which personnel professionals survey compensation levels
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