De verklarende variabelen verkennen

Als de responsvariabele logisch is, liggen alle punten op de lijnen y is nul en y is één. Daardoor is het lastig om te zien wat er gebeurt. In de video was pas met de trendlijn duidelijk hoe de verklarende variabele over elke lijn verdeeld was. Dit los je op met een histogram van de verklarende variabele, gefacet op de respons.

Je gebruikt deze histogrammen om de churn-gegevensset van financiële diensten uit de video beter te leren kennen.

churn is beschikbaar en ggplot2 is geladen.

Deze oefening maakt deel uit van de cursus

Introductie tot regressie in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot regressie 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: Waarom je logistische regressie nodig hebt Exercise 2: De verklarende variabelen verkennen

Huidige oefening

Exercise 3: Lineaire en logistische modellen visualiseren Exercise 4: Logistische regressie met glm()Exercise 5: Voorspellingen en odds ratio’s Exercise 6: Kansen Exercise 7: Meest waarschijnlijke uitkomst Exercise 8: Odds ratio Exercise 9: Log-oddsratio Exercise 10: De kwaliteit van een logistisch regressiemodel kwantificeren Exercise 11: De confusion matrix berekenen Exercise 12: De prestaties van een logistiek model meten Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Gefeliciteerd