CX Business Analytics

By the end of this concentration, the students will be able to:

1. Define the least square estimates of the parameters
2. List down the transformations and weighting to correct the model inadequacies
3. Describe the use of indicator variable in regression
4. Develop simple and multiple linear regression models
5. Develop the hypothesis test and confidence intervals for unknown parameters
6. Analyze the adequacy of models
7. Develop a polynomial regression model
8. Explain what is multicollinearity, its sources, effects, diagnostics and remedial measures.
9. Develop a model using different variable selection and model building techniques
10. Interpret the estimated regression model
11. Assess leverage and influence observations

Course Description:

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.