Business Analytics

iii) Draw a scatter plot based on above data. Based on your plot comment on the relationship between height and weight.

The relationship between height and weight is almost perfect. The connection between the height and weight variables involved a high degree of randomness.

iv) Compute the equation of the regression line.

Intercept (a): 13.942585996204 Slope (b): -0.041046774654845 Regression line equation: y=13.942585996204-0.041046774654845x

v) Calculate the R2 value and comment on the goodness of fit.

R=| (-0.18-0) |/0.14=8.2

R2=82%

The goodness of fit between the height and weight of shows that the values have a hogh degree of randomness.

vi)Use an analytical tool of your choice to calculate them with your answer.

R=| (-0.18-0) |/0.14=8.4

3. classification and regression are commonly used processes in business analysis.

i) Briefly explain the difference between classification and prediction.

For an element in a given data set classification determines a categorical class or label whereas prediction, predicts a missing element in a data set.

ii) Give examples for classification methods you know.

An algorithm or a classifier which utilize data to understand a particular output.

A classification tree records, labels and assign variables to discrete classes.

iii) Give at least three examples how clustering can be used in business analytics. In your answer explain each business case could be addressed using clustering.

Cluster analysis is an exploratory information examination instrument which goes for arranging various items into groups such that the level of relationship between two objects is maximal in the event that they have a place with a similar gathering.

Clustering is not dependent on any driving objective function since it generates natural clusters. For instance, to determine customer profit in next 3 months a decision tree is built. Here, the cluster gives an analysis of the portfolio on distinct attributes target.

Clustering technique is used in business analytics for portfolio initial profiling. To build a specific strategy, an objective modelling technique is used, after having a good understanding of the portfolio.

Being one of the widely used modelling techniques in industries, it segments a customer portfolio that is based on transaction behavior, demographics and other behavioral attributes.

References

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., … & Bloomfield, C. D. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. science, 286(5439), 531-537.

Klein, J. P., Rizzo, J. D., Zhang, M. J., & Keiding, N. (2001). Statistical methods for the analysis and presentation of the results of bone marrow transplants. Part 2: Regression modeling. Bone marrow transplantation, 28(11), 1001.

Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational research methods, 13(3), 456-476.

Van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE learning initiative, 1(1), l-ll.

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