6840-10-12-1: Ch.3 - Overview

(4:33)

6840-10-07-5: Chapter 2 - LR - Diagnosis - Other methods

(2:19)

6840-10-12-4: Ch.3 - Inference - A test by the difference of deviations

(4:25)

6840-10-26-3: Prospective and Retrospective Sampling

(10:54)

6840-10-14-4: Ch.3 - Overdispersion - Beta Distribution

(3:41)

6840-10-19-2: 3.5 QMLE - General theory

(5:17)

6840-10-12-3: Inference - A test by deviance

(8:58)

6840-10-14-1: Ch.3 - Overdispersion - Concepts

(12:10)

6840-10-28-3: Ch.5.1 - Count regression - An Overview

(4:37)

ASÍ ERA EL EXAMEN DE LA UNI EN LOS OCHENTAS | UNI 1988

(1:10:1econd)

Quasi-Poisson and negative binomial regression models

(16:41)

Model selection and evaluation: overdispersion, quasi-Poisson

(25:33)

Logistic Regression [Simply explained]

(14:22)

Calculating sample size and power

(28:)

Negative Binomial Regression model | Statistical model| Count Data model

(19:41)

Logistic Regression: Estimating Parameters

(17:56)

OMSI 2 | MILLENNIUM BRT II - MB O-500UA | Linha 6840-10: Term. JD. Jacira x Term. Capelinha

(44:54)

6840-10-14-2: Ch.3 - Overdispersion - Estimation \u0026 Testing

(7:32)

6840-10-14-3: Ch.3 - Overdispersion - Example - Trout egg

(13:25)

6840-10-19-1: QMLE - An overview

(5:55)

6840-10-12-6: Ch.3 - Pearson's chi-square statistic \u0026 test

(6:5)

6840-10-26-4: Ch.4.4 - Prediction (C.I.) \u0026 Effective Doses

(12:38)

6840-10-07-7: Chapter 2 - LR - Bias reduction logistic regression (and other remedies)

(9:45)

6840-10-12-2: Ch.3 - Binomial regression model

(8:51)

6840-10-07-6: Chapter 2 - LR - Linearly separable cases

(7:21)

6840-10-26-2: Ch.4.2 - Link Functions

(19:20)

6840-10-05-4: Logistic regression - diagnostics - hat values

(4:20)