DECS 434 Waiver Information

Course Title:
Statistical Methods for Managerial Decisions

Course Description:
This sequel to DECS-433 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables. Topics include one- and two-population hypothesis testing, correlation, simple and multiple regression analysis, and qualitative variables. The course also covers applications of the material and a number of case studies. Extensive use of spreadsheet statistical analysis software is required.

Turbo Version:
Any student may elect to take the turbo version (DECS 437) instead of DECS 434 (without taking any waiver exam). This is entirely up to the student.

Waiving DECS 434 (and 437) completely:
The only way to waive DECS 434 without taking the turbo version (DECS 437) is by taking a waiver examination.  A waiver exam can, in principle, be taken at any point in time before the end of the fall quarter of the student's first year at Kellogg. To schedule a waiver exam please contact (at least a week before taking the exam). If you have questions contact Prof Christoph Kuzmics.

Every student (whatever their prior education and experience) is, in principle, entitled to take a waiver exam. Only students who have a good understanding of the topics listed below, however, are encouraged to take a waiver exam.  In particular a student will only be successful in taking a waiver exam if all of the following keywords are familiar to him/her: hypothesis testing, confidence intervals, linear regression, log-regression, multicollinearity, omitted variable bias, heteroskedasticity, dummy variables, and slope-dummy variables (variables which are products of one variable and a dummy-variable). Here is a more precise list of topics the student should be familiar with for a waiver examination:

1.   Hypothesis testing and interval estimation of the difference between two means or proportions.
2.   Basic linear regression model and assumptions; both simple and multiple regression.
3.   Evaluating and interpreting regression output generated by a computer package; the ability to relate regression output to a particular problem or setting.
4.   Goodness-of-fit (R-squared and adjusted R-squared), prediction, interval estimation (both confidence and prediction intervals) and hypothesis testing (t-tests, F-tests, Generalized F-tests, p-values, etc.) in the framework of the regression model.
5.   Use and interpretation of regression diagnostics/residual analysis to check if regression assumptions are satisfied.
6.   The consequences if various regression model assumptions are violated; how to remedy violations of the regression assumptions.
7.   How to introduce and use slope- and intercept-dummy (qualitative) variables; how to recognize and analyze omitted variable bias; how to diagnose and remedy multicollinearity.
8.   The ability to apply the above techniques in management settings such as, for example, using regression to evaluate the riskiness of an asset within the Capital Asset Pricing Model; to evaluate the impact of advertising; as an aid to managerial decision making.

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