Anket istatistikleri

ABD'de ortalama bir kişi geceleri kaç saat uyuyor? Uyku cinsiyete göre değişiyor mu? Bu soruları Gender ve SleepHrsNight değişkenlerini kullanarak inceleyelim. SleepHrsNight, bir katılımcının hafta içi gecelerinde genellikle kaç saat uyuduğuna dair kendi beyanını içerir ve yalnızca en az 16 yaşındaki katılımcılar için raporlanır.

Bu egzersiz

R ile Anket Verilerini Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Compute the survey-weighted mean
svy___(x = ___, 
       design = NHANES_design,
       na.rm = TRUE)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Anket Verilerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Our exploration of survey data will begin with survey weights. In this chapter, we will learn what survey weights are and why they are so important in survey data analysis. Another unique feature of survey data are how they were collected via clustering and stratification. We'll practice specifying and exploring these sampling features for several survey datasets.

Exercise 1: What are survey weights?Exercise 2: Survey weights Exercise 3: Visualizing the weights Exercise 4: Specifying elements of the design in R Exercise 5: Designs in R Exercise 6: Stratified designs in R Exercise 7: Cluster designs in R Exercise 8: Comparing survey weights of different designs Exercise 9: Visualizing the impact of survey weights Exercise 10: NHANES weights Exercise 11: Tying it all together!

Now that we have a handle of survey weights, we will practice incorporating those weights into our analysis of categorical data in this chapter. We'll conduct descriptive inference by calculating summary statistics, building summary tables, and constructing bar graphs. For analytic inference, we will learn to run chi-squared tests.

Exercise 1: Visualizing a categorical variable Exercise 2: Summarizing a categorical variable Exercise 3: Interpreting frequency tables Exercise 4: Graphing a categorical variable Exercise 5: Exploring two categorical variables Exercise 6: Creating contingency tables Exercise 7: Building segments bar graphs Exercise 8: Summarizing with svytotal()Exercise 9: Interpreting svymean()Exercise 10: Inference for categorical variables Exercise 11: Running a chi squared test Exercise 12: Tying it all together!

Of course not all survey data are categorical and so in this chapter, we will explore analyzing quantitative survey data. We will learn to compute survey-weighted statistics, such as the mean and quantiles. For data visualization, we'll construct bar-graphs, histograms and density plots. We will close out the chapter by conducting analytic inference with survey-weighted t-tests.

Exercise 1: Nicel verileri özetleme Exercise 2: Anket istatistikleri

Geçerli Egzersiz

Exercise 3: Kantilleri tahmin etme Exercise 4: Nicel verileri görselleştirme Exercise 5: Anket ağırlıklı ortalamaların çubuk grafikleri Exercise 6: svyby() çıktısı Exercise 7: Hatalı çubuk grafikler Exercise 8: Anket-ağırlıklı histogramlar Exercise 9: Anket ağırlıklı yoğunluk grafikleri Exercise 10: Nicel veriler için çıkarım Exercise 11: Anket ağırlıklı t-testi Exercise 12: Hepsini bir araya getirelim!

To model survey data also requires careful consideration of how the data were collected. We will start our modeling chapter by learning how to incorporate survey weights into scatter plots through aesthetics such as size, color, and transparency. We'll model the survey data with linear regression and will explore how to incorporate categorical predictors and polynomial terms into our models.

Exercise 1: Visualization with scatter plots Exercise 2: Bubble plots Exercise 3: Survey-weighted scatter plots Exercise 4: Use of color in scatter plots Exercise 5: Visualizing trends Exercise 6: Line of best fit Exercise 7: Trend lines Exercise 8: Modeling survey data Exercise 9: Regression model Exercise 10: Regression inference Exercise 11: More complex modeling Exercise 12: Multiple linear regression Exercise 13: Tying it all together Exercise 14: Wrap-up