Numeriek vs. categorisch visualiseren

Als de verklarende variabele categorisch is, heeft de spreidingsplot die je eerder gebruikte om de data te visualiseren geen zin. In plaats daarvan is het een goed idee om per categorie een histogram te tekenen.

De Taiwan-vastgoedgegevensset heeft een categorische variabele in de vorm van de leeftijd van elk huis. De leeftijden zijn opgedeeld in 3 groepen: 0 tot 15 jaar, 15 tot 30 jaar en 30 tot 45 jaar.

taiwan_real_estate is beschikbaar en ggplot2 is geladen.

Deze oefening maakt deel uit van de cursus

Introductie tot regressie in R

Cursus bekijken

Oefeninstructies

Gebruik taiwan_real_estate om een histogram van price_twd_msq te plotten met 10 bins.
Facet de plot op house_age_years zodat je 3 panelen krijgt.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Using taiwan_real_estate, plot price_twd_msq
___ +
  # Make it a histogram with 10 bins
  ___ +
  # Facet the plot so each house age group gets its own panel
  ___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot regressie in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Een verhaal van twee variabelen Exercise 2: Welke is de responsvariabele?Exercise 3: Twee variabelen visualiseren Exercise 4: Een lineaire regressie fitten Exercise 5: Schat de intercept Exercise 6: Schat de helling Exercise 7: Lineaire regressie met lm()Exercise 8: Categorische verklarende variabelen Exercise 9: Numeriek vs. categorisch visualiseren

Huidige oefening

Exercise 10: Gemiddelden per categorie berekenen Exercise 11: lm() met een categorische verklarende variabele

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: Why you need logistic regression Exercise 2: Exploring the explanatory variables Exercise 3: Visualizing linear and logistic models Exercise 4: Logistic regression with glm()Exercise 5: Predictions and odds ratios Exercise 6: Probabilities Exercise 7: Most likely outcome Exercise 8: Odds ratio Exercise 9: Log odds ratio Exercise 10: Quantifying logistic regression fit Exercise 11: Calculating the confusion matrix Exercise 12: Measuring logistic model performance Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Congratulations