Extend your linear regression skills to "parallel slopes" regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

Parallel slopes linear regression

Fitting a parallel slopes linear regression

Interpreting parallel slopes coefficients

Visualizing each explanatory variable

Visualizing parallel slopes

Predicting parallel slopes

Predicting with a parallel slopes model

Manually calculating predictions

Assessing model performance

Comparing coefficients of determination

Comparing residual standard error

Parallel Slopes

Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

Models for each category

One model per category

Predicting multiple models

Visualizing multiple models

One model with an interaction

Specifying an interaction

Interactions with understandable coeffs

Making predictions with interactions

Predicting with interactions

Manually calculating predictions with interactions

Simpson's Paradox

Modeling eBay auctions

Modeling each auction type

Interactions

See how modeling, and linear regression in particular, makes it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

Two numeric explanatory variables

3D visualizations

Modeling 2 numeric explanatory variables

Including an interaction

More than 2 explanatory variables

Visualizing many variables

Different levels of interaction

Predicting again

How linear regression works

The sum of squares

Linear regression algorithm

Multiple Linear Regression

Extend your logistic regression skills to multiple explanatory variables. Understand the logistic distribution, which underpins this form of regression. Finally, implement your own logistic regression algorithm.

Regresi logistik berganda

Memvisualisasikan beberapa variabel penjelas

Regresi logistik dengan 2 variabel penjelas

Prediksi regresi logistik

Confusion matrix

Distribusi logistik

Fungsi distribusi kumulatif

Fungsi distribusi kumulatif invers

argumen family binomial

Parameter distribusi logistik

Cara kerja regresi logistik

Likelihood & log-likelihood

Algoritme regresi logistik

Selamat

Multiple Logistic Regression

Taiwan real estate prices

eBay Palm Pilot auctions

Bank churn

Regresi linear dan regresi logistik adalah dua model statistik yang paling luas digunakan dan berperan seperti kunci utama yang membuka rahasia tersembunyi dalam himpunan data. Kursus ini membangun keterampilan yang Anda peroleh di "Pengenalan Regresi di R", mencakup regresi linear dan logistik dengan beberapa variabel penjelas. Melalui latihan praktik, Anda akan mengeksplorasi hubungan antarvariabel dalam himpunan data dunia nyata, seperti harga rumah di Taiwan dan pemodelan churn pelanggan, dan lainnya. Di akhir kursus ini, Anda akan mengetahui cara menyertakan beberapa variabel penjelas dalam sebuah model, memahami bagaimana interaksi antarvariabel memengaruhi prediksi, serta memahami cara kerja regresi linear dan logistik.

Introduction to Regression in R

Pelajari cara memasukkan beberapa variabel penjelas dalam model dan pengaruh interaksi di R.

Regresi Tingkat Menengah di R

Pelajari cara melakukan regresi linier dan logistik dengan beberapa variabel penjelas.

Parameter distribusi logistik

Regresi Tingkat Menengah di R

Latihan interaktif praktis