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| Course ID: | BUSN 3000. 3 hours. | | Course Title: | Applied Statistics and Data Analysis for Business | Course
Description: | Analysis of business data through appropriate statistical techniques and software. Develop aptitude for data modeling through data exploration and visualization. Apply proper sampling methods to minimize bias associated with sampling. Employ statistical inference as a tool for decision making. Utilize linear regression models and time series models to analyze and inform business decisions. | | Oasis Title: | App Stat and Data Analy Bus | | Duplicate Credit: | Not open to students with credit in BUSN 3000E, BUSN 3000H, BUSN 3001 | Semester Course
Offered: | Offered every year. | | Grading System: | A-F (Traditional) |
| | Course Objectives: | Students who complete this course will be able to:
1. Recognize the advantages and disadvantages of various sampling methods and study designs, and select a method of data collection appropriate for answering questions in a business context.
2. Select appropriate numerical summaries, graphical displays, and inference procedures to answer statistical questions in a business context.
3. Use appropriate software to calculate statistics, visualize data, fit statistical models, and execute inference procedures.
4. Apply statistical inference to both quantitative and categorical data to support decision making in a business context.
5. Fit models for regression and time series data to investigate possible relationships and make predictions. | | Topical Outline: | Graphical and numerical examination of real-world business data using appropriate software
Overview of sampling methods and experimental design, making appropriate connections between the type of randomization and the scope of the conclusions
Statistical inference for proportions, means, and comparison of means, with applications to business data
Categorical data analysis based on tabular and graphical techniques, and statistical inference through chi-squared tests
Predictive modeling using simple and multiple linear regression, including residual analysis, quality-of-fit, and multi-collinearity
Trend decomposition, moving averages, and autoregressive models for time series data | |
Syllabus:
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