**Modeling Count Data using Stata**

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 4h 40m | 1.25 GB

Learn Poisson and negative binomial regression techniques

The course is divided into two parts. In the first part, you’ll be introduced to the theory behind count models in an intuitive way while keeping the math at a minimum. The course starts with an overview of count tables, where you’ll learn how to calculate the incidence rate ratio. You’ll get to grips with Poisson regression and understand how to work with continuous, binary, and categorical variables. As you advance, you’ll explore the concept of overdispersion and how to address this issue using negative binomial models. The course also covers other count models such as truncated models and zero-inflated models.

In the second part of the course, you’ll be able to apply what you have learned using Stata. You’ll be taken through a large project where you’ll fit the Poisson, negative binomial, and zero-inflated models. Additionally, you’ll discover the tools used to compare these models.

Learn

- Calculate incidence rate ratios
- Identify when to use count models
- Apply count models using Stata
- Predict the expected number of outcomes
- Compare different models
- Understand what count models are

**Table of Contents**

01 Introduction

02 Count tables

03 Risk

04 Inceidence-rate ratio

05 Two-by-three tables

06 Single independent variable

07 Examples

08 Binary variables

09 Multiple independent variables

10 Categorical variables

11 Exposure

12 Negative binomial regression

13 Truncated models

14 Zero-inflated models

15 Comparison of models

16 Predicting the number of events

17 Predicting probabilities of certain counts

18 Introduction to the dataset

19 Continuous variables

20 Binary variables

21 Multivariate analysis

22 Negative binomial regression

23 Zero-inflated models

24 Comparing count models

25 Model interpretation – predicted number of events

26 Model interpretation – predicted probabilities of different outcomes

27 Visualizing the model – predicted number of events

28 Visualizing the model – predicted probabilities of different outcomes

29 Conclusion

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