Ki-kare testleri çıktısı

Bölüm 3'te, sex ile agecat için bir chisq.test() uyguladın. Bu egzersizde testi tekrar yapacak, çıktıyı cssexage olarak kaydedecek ve ardından çıktı liste nesnesinden seçili öğeleri göstereceksin.

abaloneKeep veri kümesi ve dplyr paketi senin için yüklendi.

Bu egzersiz

SAS Kullananlar İçin R

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

abaloneKeep için cinsiyete göre yaş kategorisinin iki yönlü frekanslarının table() çıktısını tablesexage olarak kaydet.
tablesexage kullanarak chisq.test() çalıştır ve çıktıyı cssexage olarak kaydet.
cssexage çıktısından expected değerlerini ve p.value'ı görüntüle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create frequency table of sex by agecat
tablesexage <- ___ %>% 
  with(___)

# Chi-square test using tablesexage
cssexage <- ___

# Display expected values and p.value from cssexage
___
___

Kodu Düzenle ve Çalıştır

Bu egzersiz

SAS Kullananlar İçin R

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Çıktı nesneleriyle çalışmak Exercise 2: Betimsel istatistik çıktısı Exercise 3: Özet çıktısı Exercise 4: Group_by çıktısı Exercise 5: Listelerle çalışmak Exercise 6: Hmisc describe çıktısı Exercise 7: Korelasyonlar çıktısı Exercise 8: t-testleri çıktısı Exercise 9: Ki-kare testleri çıktısı

Geçerli Egzersiz

Exercise 10: ANOVA ve doğrusal modeller Exercise 11: ANOVA Exercise 12: Doğrusal regresyon Exercise 13: Final model değerlendirmesi Exercise 14: Abalone yaş kestiricileri Exercise 15: Cinsiyete göre en iyi model Exercise 16: Kurs özeti ve öneriler