Hepsini bir araya getirelim!

Şimdi NHANES tasarımına biraz daha derinlemesine bakalım ki bu verileri analiz etmeye hazır olalım! Dikkat edersen NHANES_designi, son videoda oluşturduğumuz haliyle yeniden oluşturdum. NHANES_design, NHANESraw için tasarım bilgisini içeriyor, bunu unutma. NHANESraw içindeki iki önemli tasarım değişkeni SDMVSTRA (her bir birim için tabakalaşma atamasını içerir) ve SDMVPSU (belirli bir tabakada küme kimliğini içerir). Kümelerin tabakalar içinde yuvalandığını unutma!

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R ile Anket Verilerini Analiz Etme

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Specify the NHANES design
NHANES_design <- svydesign(data = NHANESraw, strata = ~SDMVSTRA, id = ~SDMVPSU, nest = TRUE, weights = ~WTMEC4YR)

# Print summary of design
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Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

R ile Anket Verilerini Analiz Etme

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Kursa Ücretsiz Başla

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: Anket ağırlıkları nedir?Exercise 2: Anket ağırlıkları Exercise 3: Ağırlıkları görselleştirme Exercise 4: R'de tasarımın öğelerini belirtme Exercise 5: R'de tasarımlar Exercise 6: R'de tabakalı tasarımlar Exercise 7: R'da küme tasarımları Exercise 8: Farklı tasarımların anket ağırlıklarını karşılaştırma Exercise 9: Anket ağırlıklarının etkisini görselleştirme Exercise 10: NHANES ağırlıkları Exercise 11: Hepsini bir araya getirelim!

Geçerli egzersiz

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: Summarizing quantitative data Exercise 2: Survey statistics Exercise 3: Estimating quantiles Exercise 4: Visualizing quantitative data Exercise 5: Bar plots of survey-weighted means Exercise 6: Output of svyby()Exercise 7: Bar plots with error Exercise 8: Survey-weighted histograms Exercise 9: Survey-weighted density plots Exercise 10: Inference for quantitative data Exercise 11: Survey-weighted t-test Exercise 12: Tying it all together!

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