Non-parametric statistics: Applications in business analysis
Order ID |
53563633773 |
Type |
Essay |
Writer Level |
Masters |
Style |
APA |
Sources/References |
4 |
Perfect Number of Pages to Order |
5-10 Pages |
Description/Paper Instructions
Non-parametric statistics: Applications in business analysis
Non-parametric statistics is a type of statistical analysis that does not rely on the assumption of a particular distribution or any specific parameterization of the population. Instead, it uses the actual observed data to make statistical inferences. Non-parametric methods are useful when the data do not meet the assumptions of the parametric methods or when the sample size is small. In this blog, we will explore the applications of non-parametric statistics in business analysis.
- Testing for Significant Differences in Median
In business analysis, it is often required to compare two groups or samples to determine if there is a significant difference in the median. For example, we may want to compare the salaries of male and female employees in a company to see if there is a gender pay gap. The Mann-Whitney U test is a non-parametric test that can be used to test for significant differences in the median. It is also known as the Wilcoxon rank-sum test. The test is based on the rank ordering of the data and not on the actual values of the data. The test is useful when the data do not follow a normal distribution or when the sample size is small.
- Testing for Independence
Another common application of non-parametric statistics in business analysis is testing for independence between two variables. For example, we may want to test if there is a relationship between the level of education and the income of employees. The chi-square test is a non-parametric test that can be used to test for independence. The test is based on the contingency table, which displays the frequency distribution of the data for two variables. The test is useful when the data are categorical and the assumptions of the parametric methods are not met.
- Testing for Goodness of Fit
In business analysis, it is often required to test if the observed data follow a particular distribution. For example, we may want to test if the sales data of a product follow a normal distribution. The Kolmogorov-Smirnov test is a non-parametric test that can be used to test for goodness of fit. The test is based on the maximum difference between the observed cumulative distribution function and the expected cumulative distribution function. The test is useful when the data do not follow a normal distribution or when the sample size is small.
- Testing for Correlation
In business analysis, it is often required to test if there is a relationship between two variables. For example, we may want to test if there is a correlation between the level of education and the income of employees. The Spearman rank correlation test is a non-parametric test that can be used to test for correlation. The test is based on the rank ordering of the data and not on the actual values of the data. The test is useful when the data are ordinal or when the assumptions of the parametric methods are not met.
- Testing for Significance of Association
In business analysis, it is often required to test if there is a significant association between two variables. For example, we may want to test if there is a significant association between the gender of employees and the department they work in. The Kendall’s tau test is a non-parametric test that can be used to test for the significance of association. The test is based on the number of concordant and discordant pairs of the data. The test is useful when the data are ordinal or when the assumptions of the parametric methods are not met.
Conclusion
Non-parametric statistics is a powerful tool in business analysis. It can be used to test for significant differences in median, independence, goodness of fit, correlation, and significance of association. Non-parametric methods are particularly useful when the data do not follow a normal distribution or when the assumptions of the parametric methods are not met. Non-parametric
Non-parametric statistics: Applications in business analysis
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30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. |
40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. |
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15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. |
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