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Nils Baker
Ovchinnikov, Anton S.; Pfeifer, Phillip E.; Call, Nathan Case QA-0793 / Published August 7, 2012 / 3 pages.
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Product Overview

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.

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

  • Overview

    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.

  • Learning Objectives

    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