Esplorare le variabili esplicative

Quando la variabile risposta è logica, tutti i punti si trovano sulle linee y uguale zero e y uguale uno, il che rende difficile capire cosa sta succedendo. Nel video, finché non hai visto la linea di tendenza, non era chiaro come la variabile esplicativa fosse distribuita su ciascuna linea. Questo si può risolvere con un istogramma della variabile esplicativa, con facet sulla risposta.

Userai questi istogrammi per familiarizzare con l'insieme di dati sul churn nei servizi finanziari visto nel video.

churn è disponibile e ggplot2 è caricato.

Questo esercizio fa parte del corso

Introduzione alla regressione in R

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Prova a risolvere questo esercizio completando il codice di esempio.

# Using churn, plot time_since_last_purchase
___ +
  # as a histogram with binwidth 0.25
  ___ +
  # faceted in a grid with has_churned on each row
  ___

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Questo esercizio fa parte del corso

Introduzione alla regressione in R

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You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Exercise 1: A tale of two variables Exercise 2: Which one is the response variable?Exercise 3: Visualizing two variables Exercise 4: Fitting a linear regression Exercise 5: Estimate the intercept Exercise 6: Estimate the slope Exercise 7: Linear regression with lm()Exercise 8: Categorical explanatory variables Exercise 9: Visualizing numeric vs. categorical Exercise 10: Calculating means by category Exercise 11: lm() with a categorical explanatory variable

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Exercise 1: Making predictions Exercise 2: Predicting house prices Exercise 3: Visualizing predictions Exercise 4: The limits of prediction Exercise 5: Working with model objects Exercise 6: Extracting model elements Exercise 7: Manually predicting house prices Exercise 8: Using broom Exercise 9: Regression to the mean Exercise 10: Home run!Exercise 11: Plotting consecutive portfolio returns Exercise 12: Modeling consecutive returns Exercise 13: Transforming variables Exercise 14: Transforming the explanatory variable Exercise 15: Transforming the response variable too

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Exercise 1: Quantifying model fit Exercise 2: Coefficient of determination Exercise 3: Residual standard error Exercise 4: Visualizing model fit Exercise 5: Residuals vs. fitted values Exercise 6: Q-Q plot of residuals Exercise 7: Scale-location Exercise 8: Drawing diagnostic plots Exercise 9: Outliers, leverage, and influence Exercise 10: Leverage Exercise 11: Influence Exercise 12: Extracting leverage and influence

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Exercise 1: Perché ti serve la regressione logistica Exercise 2: Esplorare le variabili esplicative

Esercizio in corso

Exercise 3: Visualizzare modelli lineari e logistici Exercise 4: Regressione logistica con glm()Exercise 5: Previsioni e odds ratio Exercise 6: Probabilità Exercise 7: Esito più probabile Exercise 8: Odds ratio Exercise 9: Log odds ratio Exercise 10: Quantificare l'aderenza della regressione logistica Exercise 11: Calcolare la matrice di confusione Exercise 12: Misurare le prestazioni del modello logistico Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Congratulazioni