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| Semester: | Fall 2024 |
| Number: | 0207-274-002 |
| Instructor: | Zahra Sedighi Maman |
| Days: | Monday Wednesday 4:15 pm - 5:30 pm |
| Note: | Traditional In-Person Class |
| Location: | Garden City - Hagedorn Hall of Enterprise 216 |
| Credits: | 3 |
| Status: | This Course is Filled to Capacity |
| Course Materials: | View Text Books |
| Related Syllabi: |
Jiang Zhang for
Fall 2017* |
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*Attention Students: Please note that the syllabi available for your view on these pages are for example only. The instructors and requirements for each course are subject to change each semester. If you enroll in a particular course, your instructor and course outline may differ from what is presented here. |
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| Description: |
This course is a continuation of the course Analytical and Statistical Modeling. It emphasizes the practical applications of statistics to business scenarios and the use of statistical concepts and techniques for operational and managerial decisions. (Learning Goals:Q) |
| Learning Goals: |
Upon completing the course, you should be able to:1. Develop and refine decision-making skills by basing decision upon the outcome of statistical tests.2. Analyze real world scenarios and determine the appropriate type of analytical problem solving techniques to utilize.3. Interpret the results of print-outs (ANOVA, MLR, etc) generated from a selected software program.4. Understand the reasoning/basis behind each statistical test 5. Use scatter plots and estimated correlation coefficients to describe the relationship between two variables in a dataset.6. Use linear regression to find a formula that relates the value of a dependent variable to one or more independent variables in a dataset.7. Refine a regression model to be accurate while including only the most significant variables.8. Describe the statistical precision of a regression model by performing hypothesis tests.9. Describe the most common methods of time series forecasting.10. Compute measures that describe the accuracy of forecasting systems over time.11. Forecast a time series using a regression-derived trend line.12. Forecast a time series using exponential smoothing with trend and seasonal adjustments.13. Conduct quality control using statistical methods and understand the acceptance sampling technique14. Perform a decision analysis with probability and sample information. *The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here. |
| Prerequisites: |
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