Question

Please find any two articles from any source (google or any other source, please mention the source), each of which satisfies one of the criteria below. Do not use two articles with the same analytical technique.

1. The article uses correlation or multiple regression.
2. The article uses a cluster analysis.

Describe the purpose of each of the two articles. Explain how the complex analysis used satisfies this purpose, and what it demonstrates.

The limit is one page (300 words) per article.

Answer 1

The article uses correlation or multiple regression:-

Multiple linear regression analysis may be used to describe the relation of one geologic variable to a number of other (independent) variables, and also may be used to fit a trend surface to geographically distributed variables. The leastsquares estimates of the regression coefficients differ unpredictably from the true coefficients if the independent variables are correlated. The estimates can be too large in absolute value, and may have the wrong sign. Also, the least-squares solution may be unstable in that replicate samples can give widely differing values of the regression coefficients. Ridgeregression analysis is a technique for removing the effect of correlations from the regression analysis. The procedure involves addition of a small constant K to the diagonal elements of the standardized covariance matrix. The estimates obtained are biased but have smaller sums of squared deviations between the coefficients and their estimates. The ridge trace, a plot of the coefficients versus K, helps determine the value of K that stabilizes the estimates. Correlations between geologic variables are common, and regression coefficients based on these data may be suspect. In trendsurface analysis, correlations between the geographic coordinates may differ widely, and extreme correlations may be introduced if higher order terms are used in the trend. Ridgeregression analysis serves to guide the geologist to a more reliable interpretation of the results of multiple regression if the independent variables are correlated.

The article uses a cluster analysis:-

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

In every country, the Small and Medium Enterprises (SMEs) always give a lot contribution in the industrial development. This study tries to examine the successful performance SMEs using cluster analysis to map the pattern of growth mode and strategies. Therefore the study will conduct by collect the data of growth of the business, firm and market turnover, goals and objectives, level of education, comparison with the competitor, management principle, etc as the variables for cluster analysis. The data used for this study collected from questionnaires, with the entrepreneurs as the respondents. The expected result of this study is forming the several clusters within the SMEs which characterized as survival cluster, innovators with continuous improvement cluster, network of success cluster and need support cluster. The result can be used as a guidance of the SMEs to make their policies to improve their business becoming successful in the future. Each SME has different type, so they can refer for the information to the similar business to make their own policies.

References

SPRINGER

Balaton, 2008

Balaton, K., 2008. Enterprise Strategies in Hungary in the Period of Joining the European Union, Competitive Review, 18, 1/2; ABI/INFORM Complete, pp.9.

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Barth et al., 2011

Barth, J.R., et al., 2011. Small and Medium Enterprise Financing in Transition Economies, International Atlantic Economic Society, Atl Econ J (2011) 39:19-38, DOI 10 1007/s11293-010-9260-0.

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