Regresi linear

Anda dapat menggunakan fungsi lm() untuk melakukan regresi linear sederhana. Pada latihan ini, Anda akan menyesuaikan model untuk age sebagai fungsi dari, atau diprediksi oleh, shuckedWeight. Anda akan menyimpan hasil model sebagai sebuah objek dan kemudian menampilkan elemen dari objek keluaran tersebut seperti coefficients.

Anda juga akan menjalankan summary() pada objek model dan menyimpan keluarannya dalam objek lain, yang akan berisi elemen terkait kecocokan model seperti r.squared dan adj.r.squared. Himpunan data abaloneKeep telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

R untuk Pengguna SAS

Petunjuk latihan

Gunakan lm() untuk melakukan regresi linear sederhana dari age berdasarkan shucked weight. Simpan keluarannya sebagai lmshucked.
Tampilkan elemen coefficients dari lmshucked untuk memperoleh koefisien intercept dan slope model.
Jalankan summary() untuk lmshucked dan simpan keluarannya sebagai smrylmshucked.
Dari objek keluaran ringkasan smrylmshucked, tampilkan elemen r.squared dan adj.r.squared.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Run lm() of age by shuckedWeight, save output as lmshucked
lmshucked <- lm(___ ~ ___, data = ___)

# Display coefficients element from lmshucked
___

# Save summary() output of lmshucked as smrylmshucked
___

# Show r.squared and adj.r.squared elements of smrylmshucked
___
___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

R untuk Pengguna SAS

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

This first chapter will get you oriented into the R programming environment. You'll learn how to get help, load a dataset, and increase functionality by adding packages. You'll begin working with the abalone dataset (through the dplyr package workflow) to get descriptive statistics and create helpful visualizations (using the ggplot2 package).

Exercise 1: Get help and load data in R Exercise 2: Getting help Exercise 3: Load dataset and get details Exercise 4: Add functionality with packages Exercise 5: Dataset contents and descriptive statistics Exercise 6: Load external dataset Exercise 7: Dataset contents Exercise 8: Descriptive statistics Exercise 9: Graphical visualizations Exercise 10: Histograms Exercise 11: Boxplots and violin plots Exercise 12: Scatterplots

Now that you are oriented in the R environment, this chapter will advance your understanding of R's versatility working with data objects. You'll learn how to create and modify variables in the abalone data set. Using your ggplot2 visualization skills, you will discover the data errors in the abalone data and then create a final cleaned data set ready for analysis and modeling.

Exercise 1: Objects - the building blocks of R Exercise 2: Create data objects in R Exercise 3: Create composite object types Exercise 4: Selecting elements from objects Exercise 5: Determine variable types Exercise 6: Select elements in objects Exercise 7: Manipulating datasets and data objects Exercise 8: Create new variables Exercise 9: Recode variables Exercise 10: Object type conversion Exercise 11: Data quality and cleaning Exercise 12: Variables inspection Exercise 13: Illogical weights Exercise 14: Check dimension measurements Exercise 15: Check final dataset

Once your data set has been cleaned, the next step is exploration. In chapter 3 you will learn how to compute descriptive statistics, explore associations (e.g., correlations) among the variables, and perform bi-variate statistical tests (e.g., t-tests and chi-square tests). You will also create graphical visualizations which illustrate the bi-variate associations and group comparison tests.

Exercise 1: Exploratory data analysis Exercise 2: Descriptive statistics and function masking Exercise 3: Specific statistics for one or more variables Exercise 4: Summary statistics by group Exercise 5: Correlations and t-tests Exercise 6: Bivariate correlations Exercise 7: Scatterplots Exercise 8: Correlations by sex Exercise 9: Tests for two groups Exercise 10: Categorical data: analyze and visualize Exercise 11: Chi-square tests Exercise 12: Mosaic plots Exercise 13: Age categories by shellweight categories

In this final chapter, you will learn how to work with one of the most versatile data object types in R called a list. These skills will enable you to save and manipulate your output from descriptive statistics, associations, and group comparison computations. You will also learn how to perform ANOVA (analysis of variance) and linear regression in R. All your skills are put to use in the final exercises to create the best models for predicting abalone ages from their sex, size, and weight measurements.

Exercise 1: Bekerja dengan objek keluaran Exercise 2: Keluaran statistik deskriptif Exercise 3: Merangkum output Exercise 4: Keluaran group_by Exercise 5: Bekerja dengan list Exercise 6: Keluaran Hmisc describe Exercise 7: Keluaran korelasi Exercise 8: keluaran t-test Exercise 9: Keluaran uji chi-kuadrat Exercise 10: ANOVA dan model linear Exercise 11: ANOVA Exercise 12: Regresi linear

Latihan Saat Ini

Exercise 13: Evaluasi model final Exercise 14: Prediktor usia abalon Exercise 15: Model terbaik berdasarkan jenis kelamin Exercise 16: Ringkasan kursus dan rekomendasi