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Global Warming Revisited (B)
Ovchinnikov, Anton S. Case QA-0809 / Published June 26, 2013 / 5 pages.
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Product Overview

Is there statistical evidence of global warming? The B case presents the results of three different analyses that are representative of MBA students' work. The case series is effective in discussing the analysis of time-series data in the context of business analytics, predictive analytics, big data, or other similar courses. Given that the case is based on global warming?a phenomenon all students are undoubtedly aware of?it is eye-opening with respect to how incorrect data analysis can indeed "support" different points of view.

Learning Objectives

A case objectives: • To strengthen students’ ability to analyze time-series data and the key concepts of such analysis: random walks, trends versus cycles, Holt’s and Winter’s exponential smoothing models, runs tests, and autocorrelations • To reinforce the notion of statistical significance • To reinforce assumptions underlying linear regression and hypothesis testing B case objectives: • To be able to select, from a set of potential methodologies, the one that is most appropriate for the analytical task at hand • To be able to critically examine the presented analyses of others and use the identified “mistakes” to guide in creating a better analytical approach

  • Overview

    Is there statistical evidence of global warming? The B case presents the results of three different analyses that are representative of MBA students' work. The case series is effective in discussing the analysis of time-series data in the context of business analytics, predictive analytics, big data, or other similar courses. Given that the case is based on global warming?a phenomenon all students are undoubtedly aware of?it is eye-opening with respect to how incorrect data analysis can indeed "support" different points of view.

  • Learning Objectives

    Learning Objectives

    A case objectives: • To strengthen students’ ability to analyze time-series data and the key concepts of such analysis: random walks, trends versus cycles, Holt’s and Winter’s exponential smoothing models, runs tests, and autocorrelations • To reinforce the notion of statistical significance • To reinforce assumptions underlying linear regression and hypothesis testing B case objectives: • To be able to select, from a set of potential methodologies, the one that is most appropriate for the analytical task at hand • To be able to critically examine the presented analyses of others and use the identified “mistakes” to guide in creating a better analytical approach