# DECS-431 Waiver Information

**Course Title:**

Business Analytics II

**Course Description**:

This sequel to DECS-430 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables, primarily through multivariate regression. In addition to learning basic regression skills, including modeling and estimation, students will deepen their understanding of hypothesis testing and how to make inferences and predictions from data. Students will also learn new principles such as identification and robustness. The course has an intense focus on managerially relevant applications, cases and interpretations.

**Prerequisites:**

All Students: DECS-430

**Waiving DECS-431 completely**:

The ONLY way to waive DECS-431 is by taking a waiver examination. Please contact Professor Wioletta Dziuda for more details about the content of the exam. Contact Kalpana Waikar if you would like to sign up for the waiver.

Every student (whatever their prior education and experience) may take the 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 passing the 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 variable (variables which are products of one variable and a dummy-variable). Here is a more precise list of topics a 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 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, when and how to use a LOGIT regression.

8. The ability to apply the above techniques in management settings, such as 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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