Review linear models and learn how GLMs are an extension of the linear model given different types of response variables. You will also learn the building blocks of GLMs and the technical process of fitting a GLM in Python.

Going beyond linear regression

Applying linear models

Linear model, a special case of GLM

How to build a GLM?

Data type and distribution family

Linear model and a binary response variable

Comparing predicted values

How to fit a GLM in Python?

Model fitting step-by-step

Results of the model fit using summary()

Extracting parameter estimates

Introduction to GLMs

This chapter focuses on logistic regression. You'll learn about the structure of binary data, the logit link function, model fitting, as well as how to interpret model coefficients, model inference, and how to assess model performance.

Binary data and logistic regression

Compute odds and probabilities

Fit logistic regression

Interpreting coefficients

Coefficients in terms of odds

Model formula

Interpreting logistic model

Rate of change in probability

Interpreting model inference

Statistical significance

Computing Wald statistic

Confidence intervals

Computing and describing predictions

Visualize model fit using regplot()

Compute predictions

Compute confusion matrix

Modeling Binary Data

Here you'll learn about Poisson regression, including the discussion on count data, Poisson distribution and the interpretation of the model fit. You'll also learn how to overcome problems with overdispersion. Finally, you'll get hands-on experience with the process of model visualization.

Data hitungan dan distribusi Poisson

Visualisasikan variabel respons

Mempelajari regresi Poisson

Menafsirkan kecocokan model

Estimasi parameter lambda

Menafsirkan koefisien Poisson

Interval kepercayaan Poisson

Masalah Overdispersi

Apakah mean sama dengan varians?

Menghitung jumlah hitungan yang diharapkan

Memeriksa overdispersion

Memasang negative binomial

Interval kepercayaan untuk model Binomial Negatif

Memplot model regresi

Memplot data dan kecocokan model linear

Memplot nilai hasil pemodelan

Modeling Count Data

In this final chapter you'll learn how to increase the complexity of your model by adding more than one explanatory variable. You'll practice with the problem of multicollinearity, and with treating categorical and interaction terms in your model.

Multivariable logistic regression

Fit a multivariable logistic regression

The effect of multicollinearity

Compute VIF

Comparing models

Checking model fit

Compare two models

Deviance and linear transformation

Model matrix for continuous variables

Variable transformation

Coding categorical variables

Categorical and interaction terms

Modeling with categorical variable

Interaction terms

Congratulations!

Multivariable Logistic Regression

Well switch due to arsenic poisoning

Nesting of the female horseshoe crab

Credit default

Level of salary and years of work experience

Medical costs per person given age and BMI

Bike crossings in New York City

Bayangkan Anda dapat menangani data dengan variabel respons yang bersifat biner, cacah, atau kira-kira normal, semuanya dalam satu kerangka. Anda tidak perlu membayangkannya lagi. Inilah kursus Generalized Linear Models di Python! Dalam kursus ini, Anda akan memperluas perangkat regresi Anda dengan model logistik dan Poisson, dengan mempelajari cara melakukan fitting, memahami, menilai kinerja model, dan akhirnya menggunakan model untuk membuat prediksi pada data baru. Anda akan berlatih menggunakan data dari studi dunia nyata seperti keracunan populasi terbesar dalam sejarah, perilaku bersarang kepiting tapal kuda, dan perhitungan pesepeda yang melintasi jembatan di New York City.

Introduction to Linear Modeling in Python

Perluas alat regresi Anda dengan model logistik dan Poisson, pelajari cara melatih dan memprediksi.

Data hitungan dan distribusi Poisson

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