Memvisualisasikan numerik vs. kategorial

Jika variabel penjelas bersifat kategorial, bagan sebar yang Anda gunakan sebelumnya untuk memvisualisasikan data tidak lagi tepat. Sebagai gantinya, pilihan yang baik adalah menggambar histogram untuk setiap kategori.

Himpunan data real estat Taiwan memiliki variabel kategorial berupa usia setiap rumah. Usia dibagi menjadi 3 kelompok: 0 hingga 15 tahun, 15 hingga 30 tahun, dan 30 hingga 45 tahun.

taiwan_real_estate tersedia dan ggplot2 sudah dimuat.

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

Petunjuk latihan

Gunakan taiwan_real_estate untuk membuat histogram price_twd_msq dengan 10 bin.
Facet plot berdasarkan house_age_years sehingga menghasilkan 3 panel.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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
  ___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Kisah dua variabel Exercise 2: Manakah variabel responsnya?Exercise 3: Memvisualisasikan dua variabel Exercise 4: Mengepaskan regresi linear Exercise 5: Perkirakan intersep Exercise 6: Perkirakan kemiringan (slope)Exercise 7: Regresi linear dengan lm()Exercise 8: Variabel penjelas kategorikal Exercise 9: Memvisualisasikan numerik vs. kategorial

Latihan Saat Ini

Exercise 10: Menghitung rataan per kategori Exercise 11: lm() dengan variabel penjelas kategorikal

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