Kategorilere göre ortalama hesaplama

Kategorik değişkenleri keşfetmenin iyi bir yolu, her kategori için ortalama gibi özet istatistikler hesaplamaktır. Burada, Taiwan emlak veri kümesindeki konut fiyatları için gruplanmış ortalamalara bakacaksın.

taiwan_real_estate hazır ve dplyr yüklü.

Bu egzersiz

R'da Regresyona Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

taiwan_real_estate veri kümesini house_age_years ile grupla.
Her grup için price_twd_msq değişkeninin ortalamasını hesaplayacak şekilde özetle ve sütunun adını mean_by_group koy.
Sonucu summary_stats değişkenine ata ve sayılara bir bak.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

summary_stats <- taiwan_real_estate %>% 
  # Group by house age
  ___ %>% 
  # Summarize to calculate the mean house price/area
  ___(mean_by_group = ___)

# See the result
summary_stats

Kodu Düzenle ve Çalıştır

Bu egzersiz

R'da Regresyona Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: İki değişkenin öyküsü Exercise 2: Hangisi tepki değişkeni?Exercise 3: İki değişkeni görselleştirme Exercise 4: Doğrusal regresyonu uydurma Exercise 5: Kesişimi tahmin et Exercise 6: Eğimi tahmin et Exercise 7: lm() ile doğrusal regresyon Exercise 8: Kategorik açıklayıcı değişkenler Exercise 9: Sayısal vs. kategorik görselleştirme Exercise 10: Kategorilere göre ortalama hesaplama

Geçerli Egzersiz

Exercise 11: Kategorik bir açıklayıcı değişkenle lm()

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