CX Business Analytics
By the end of this concentration, the students will be able to:
- Define the least square estimates of the parameters
- List down the transformations and weighting to correct the model inadequacies
- Describe the use of indicator variable in regression
- Develop simple and multiple linear regression models
- Develop the hypothesis test and confidence intervals for unknown parameters
- Analyze the adequacy of models
- Develop a polynomial regression model
- Explain what is multicollinearity, its sources, effects, diagnostics and remedial measures.
- Develop a model using different variable selection and model building techniques
- Interpret the estimated regression model
- Assess leverage and influence observations
CX Business Analytics
| ISOM (Information System and Operations Management) | STAT 310-Regression Analysis
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Course Description:
STAT 310 Regression Analysis (3-0-3)
Simple linear regression: The least squares method, parameter estimation, confidence intervals, tests of hypotheses and model adequacy checking. Multiple linear regression, including estimation of parameters, confidence intervals, tests of hypotheses and prediction. Model adequacy checking and multicollinearity. Polynomial regression. Variable selection and model building.
Prerequisite: STAT 201 or STAT 212 or STAT 214 or STAT 319