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.

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