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Global Warming Revisited (A)
Ovchinnikov, Anton S. Case QA-0808 / Published June 26, 2013 / 2 pages.
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Product Overview

Is there statistical evidence of global warming? This case presents two sets of real global temperature data, one from the Met Office and another one from NASA, and asks students to assess if the data indeed support the belief that temperatures have been rising over the last 150+ years. The case is open-ended?it provides the data and references to some popular press articles on the subject. Also available is a B case that presents three sets of analysis typical for MBA students?an efficient starting point for class discussion.

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? This case presents two sets of real global temperature data, one from the Met Office and another one from NASA, and asks students to assess if the data indeed support the belief that temperatures have been rising over the last 150+ years. The case is open-ended?it provides the data and references to some popular press articles on the subject. Also available is a B case that presents three sets of analysis typical for MBA students?an efficient starting point for class discussion.

  • 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