Tabakalı örnekleme

14 ila 20 yaş arasındaki gençlerden, ülke genelini temsil eden bir örnekleme gençlik anketi gönderen bir ajansın parçasısın. Yüklenen youth_survey veri kümesi, bireylerin Age, Height (in inches), Weight (in pounds), Gender ve kişinin kendi beyanına dayalı How would you describe your weight? çoktan seçmeli yanıtlarını takip ediyor.

Bu egzersizde, veri kümesinden katılımcıların %1'ini içeren tabakalı, ağırlıklandırılmış bir örnek oluşturacaksın. pandas pd olarak içe aktarılmış durumda.

Veri kümesi tüm katılımcıların cinsiyet bilgilerini içeriyor, bu yüzden tabakalaştırma sütunu olarak Gender sütununu kullanacaksın.

Bu egzersiz

Python ile Anket Verilerini Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate the percentage of female and male respondents
print(youth_survey.Gender____(normalize=____) * 100)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Anket Verilerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.7+

Kursa Ücretsiz Başlayın

What is survey data, and how do we determine which statistical test to use to analyze the data? To answer this, you’ll be able to define all sorts of survey data types, encounter important concepts like descriptive and inferential statistics, and visualize survey data to determine the appropriate statistical modeling technique needed. In doing so, you will know how to best qualitatively and quantitatively define the trends and insights you come across in surveys.

Exercise 1: Introducing Survey Data Analysis Exercise 2: Looking at levels of measurements Exercise 3: Crosstabulation Exercise 4: Descriptive and Inferential Statistics Exercise 5: Descriptive statistics Exercise 6: Inferential statistics Exercise 7: Statistical Modeling Techniques Exercise 8: Scatter plot inspection Exercise 9: Choose a statistical method Exercise 10: Sampling technique match

In this chapter, you’ll learn the different ways of creating sample survey data out of population survey data by analyzing the parameters by which the survey data was taken.

Exercise 1: Rastgele örnekleme Exercise 2: Çalışanların rastgele örneklemi Exercise 3: Yinelenebilir rastgele örnekleme Exercise 4: Tabakalı Rastgele Örnekleme Exercise 5: Evet ve hayırların dağılımı Exercise 6: Tabakalı örnekleme

Geçerli Egzersiz

Exercise 7: Ağırlıklı örnekleme Exercise 8: Blog anketi Exercise 9: El tercihine göre ağırlıklı örnekleme Exercise 10: Kümeleme Örneklemesi Exercise 11: Kümeleri grupla Exercise 12: Kümeleri (cluster) seçme Exercise 13: Küme örneklemesi analizi

Now it’s time to understand the difference between descriptive and inferential statistics concerning survey data analysis with some real-life examples. Through hands-on exercises, you’ll further interpret the meaning of different variables, key measures such as central tendency and zscore, and interpret results for actionable steps.

Exercise 1: Descriptive statistics in survey analysis Exercise 2: Frequency distribution Exercise 3: Measures of variability Exercise 4: Measures of central tendency Exercise 5: Inferential statistics in survey analysis Exercise 6: Visualize data: histogram Exercise 7: Find the z-score Exercise 8: Correlations Exercise 9: Analyze variables with .corr()Exercise 10: Are employees happy?Exercise 11: Fair and square

Last but not least, it’s time to apply statistical modeling to survey data analysis with regression analysis, the two-sample t-test, chi-square test, and interpret the assumptions associated with these tests.

Exercise 1: Regression analysis Exercise 2: Fitting a linear regression model Exercise 3: Visualizing survey data Exercise 4: Safety precautions needed?Exercise 5: Two sample t-test Exercise 6: Are women more extroverted?Exercise 7: Two sample t-test on extraversion Exercise 8: Chi-square test Exercise 9: To chi-square or not to chi-square?Exercise 10: Mental health in tech survey Exercise 11: Mental health vs. remote work Exercise 12: Congratulations