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.

Due variabili esplicative numeriche

Visualizzazioni 3D

Modellare 2 variabili esplicative numeriche

Includere un'interazione

Più di 2 variabili esplicative

Visualizzare molte variabili

Diversi livelli di interazione

Di nuovo previsioni

Come funziona la regressione lineare

La somma dei quadrati

Algoritmo di regressione lineare

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.

Multiple logistic regression

Visualizing multiple explanatory variables

Logistic regression with 2 explanatory variables

Logistic regression prediction

Confusion matrix

The logistic distribution

Cumulative distribution function

Inverse cumulative distribution function

binomial family argument

Logistic distribution parameters

How logistic regression works

Likelihood & log-likelihood

Logistic regression algorithm

Congratulations

Multiple Logistic Regression

Taiwan real estate prices

eBay Palm Pilot auctions

Bank churn

La regressione lineare e la regressione logistica sono i due modelli statistici più usati e funzionano come chiavi maestre, capaci di svelare i segreti nascosti negli insiemi di dati. Questo corso sviluppa le competenze acquisite in "Introduzione alla regressione in R", approfondendo la regressione lineare e logistica con più variabili esplicative. Con esercizi pratici, esplorerai le relazioni tra variabili in insiemi di dati reali, come i prezzi delle case a Taiwan e il modeling del churn dei clienti, e altro ancora. Alla fine del corso, saprai includere più variabili esplicative in un modello, capire come le interazioni tra variabili influenzano le previsioni e comprendere a fondo come funzionano la regressione lineare e quella logistica.

Introduction to Regression in R

Scopri come includere variabili esplicative in un modello e come le interazioni influenzano le previsioni.

Regressione intermedia in R

Impara a fare la regressione lineare e logistica con più variabili che spiegano i dati.

Come funziona la regressione lineare

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