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Statistics Term Paper - Essay Example

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This paper includes the discussion of ANOVA as a significant statistical tool for business and economics especially in inferential statistics and in tests of difference for three or more independent group variables or data. ANOVA is concerned not only with finding the similarity…
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Statistics Term Paper
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Interpretation of data and results is one fundamental concern of using statistics in the fields of business and economics. One important statistical tool that for many years has become proven to help businessmen and economists is the analysis of variance (ANOVA). Analysis of variance is a significant component of inferential statistics and it tries to measure the difference between two or more independent groups or variables. In this paper, the proponent tries to discuss ANOVA as a significant statistical tool for business and economics especially in inferential statistics and in tests of difference for three or more independent groups, variables or data.

Analysis and interpretation of data are important roles associated with inferential statistics. This statistics has different statistical tools or test for the analysis of interval, ratio, nominal and ordinal data. For making inferences from or conclusions on larger groups or populations, or even in generalizing information gathered in one or more samples, inferential statistics has been proven effective in doing so. The goodness of samples is very important to consider in order so as to ensure effectiveness of inferential statistics.

In addition, sampling techniques or procedures are necessary or of great importance prior to the use of inferential statistical tools. In reality, the only way to find what specific tool in inferential statistics is necessary for any research problems or studies depends on the nature of data. It is in this reason that inferential statistics is subdivided into two groups: parametric and non-parametric tests. Non-parametric tests are tests that do not require normal distribution and they utilize both nominal and ordinal data (Bajpai 678).

One could say that the distribution is not normal when the value of skewness is either positive or negative. Skewness is positive when the mean is greater than the median and

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