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Generalized Linear Models

Course Code | : BİS534 |

Course Type | : Area Elective |

Couse Group | : Second Cycle (Master's Degree) |

Education Language | : Turkish |

Work Placement | : N/A |

Theory | : 3 |

Prt. | : 0 |

Credit | : 3 |

Lab | : 0 |

ECTS | : 6 |

Objectives of the Course

To provide advanced students in statistics, biostatistics with a course of study in the theory and practice of modern extensions of the general linear statistical model.

Course Content

Advanced topics and types in generalized linear models, structure of data, theory and applications of parameter estimate methods. Logistic regression, Poisson regression, analysis of dependent data, generalized estimating equations, the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, residuals. Model choice, fitting and validation.

Name of Lecturer(s)

Learning Outcomes

1. | To learn basic concepts and basic structure of generalized linear model |

2. | Learning the principles and methods of statistical modeling for generalized linear models |

3. | To be able to make linear transformations |

4. | To learn factor interactions, aggregate and aggregate models |

5. | To be able to establish a generalized linear model using statistical softwares and evaluate these models |

Recommended or Required Reading

Weekly Detailed Course Contents

Week 1 - Theoretical

Exponential Distribution Family and Properties

Week 2 - Theoretical

Exponential Distribution Family and Properties

Week 3 - Theoretical

Basic structure of generalized linear models

Week 4 - Theoretical

Estimation for generalized linear models

Week 5 - Theoretical

Inference for generalized linear models

Week 6 - Theoretical

The basic structure of logistic regression models

Week 7 - Theoretical

Prediction and inference in the logistic regression models

Week 8 - Intermediate Exam

Midterm exam

Week 9 - Theoretical

The basic structure of log-linear models

Week 10 - Theoretical

Prediction and inference in the log-linear models,

Week 11 - Theoretical

Establishing linear and generalized linear model in R, estimation and inference

Week 12 - Theoretical

Establishing a logistic regression model in R

Week 13 - Theoretical

Prediction and inference of a logistic regression model in R

Week 14 - Theoretical

Establishing log-linear models in R, prediction and inference

Week 15 - Theoretical

Literature review and discussion

Week 16 - Final Exam

Final exam

Assessment Methods and Criteria

Type of Assessment | Count | Percent |
---|---|---|

Midterm Examination | 1 | %40 |

Final Examination | 1 | %60 |

Workload Calculation

Activities | Count | Preparation | Time | Total Work Load (hours) |
---|---|---|---|---|

Lecture - Theory | 14 | 0 | 3 | 42 |

Assignment | 1 | 10 | 0 | 10 |

Individual Work | 8 | 0 | 2 | 16 |

Quiz | 14 | 2 | 1 | 42 |

Midterm Examination | 1 | 20 | 2 | 22 |

Final Examination | 1 | 20 | 2 | 22 |

TOTAL WORKLOAD (hours) | 154 |

Contribution of Learning Outcomes to Programme Outcomes

PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | |

OÇ-1 | ||||||||||

OÇ-2 | ||||||||||

OÇ-3 | ||||||||||

OÇ-4 | ||||||||||

OÇ-5 | 3 | 4 | 3 | 4 | 3 | 4 | 4 | 3 | 3 | 5 |

Person Responsible for Information Package

Adnan Menderes University - Information Package / Course Catalogue