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Nils Baker

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$6.25
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$6.75
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$6.75
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$3.25
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This very short and seemingly straightforward case is an efficient vehicle for exploring the nuances of hypothesis testing via regression modeling and t-tests in the context of an MBA or advanced undergraduate analytics course.
Case Number: QA-0793
Author: Ovchinnikov, Anton S.
Type: Case
Author: Pfeifer, Phillip E.
Call, Nathan
Learning Objectives:
• Improving students’ capacity in testing statistical hypothesis both with regression modeling and with t-tests
• Exploring the similarities and differences between the regression and t-test approaches to hypothesis testing (one-tailed versus two-tailed tests, equal versus unequal variances, etc.)
• Interpreting statistical software output, especially regarding regressions and t-tests
• Checking the assumptions behind a linear regression model -Independence -Linearity -Homoscedasticity -Normality of errors and specifically applying the goodness-of-fit test for Normality
• Transforming variables as a way of improving the regression model
• Distinguishing between correlation and causality in the context of regression models
• Understanding the limitations of data analysis for establishing causality and the data structures needed to draw conclusions about causality
Length: 3 pages.
Category: Management Science
Decision Analysis
Industry: Banking/Finance/Insurance
Published: 08/07/12
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