Adil ve açık

Genç yetişkinler için bir spor yarışması düzenlemek istiyorsun. Ancak, katılanların benzer becerilere sahip olduğundan emin olmak istiyorsun ki kazananlar gerçekten hak ederek kazansın. young_people adlı genç yetişkinlere yönelik bir anket yapıyor ve katılımcılara Gender, Age, Height ve Weight bilgilerini soruyorsun.

Bu egzersizde, yaş sütunundaki aykırı değerleri bulacaksın.

pandas ve scipy.stats senin için sırasıyla pd ve stats olarak yüklendi.

Bu egzersiz

Python ile Anket Verilerini Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Age sütununun z-score değerini bul.
Bu anket için Age_zscore kullanarak anketteki aykırı değerleri bul.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# z-score of `Age` column
young_people['Age_zscore'] = ____.____(young_people.____)

# Outliers in `Age_zscore`
age_outliers = young_people[
  (young_people.____ >= ____)
  |(young_people.____ <= ____)
]
print(age_outliers)

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: Random sampling Exercise 2: Random sample of employees Exercise 3: Reproducible random sampling Exercise 4: Stratified Random Sampling Exercise 5: Spread of yes's and no's Exercise 6: Stratified sampling Exercise 7: Weighted sampling Exercise 8: Blog survey Exercise 9: Weighted sampling on handedness Exercise 10: Cluster Sampling Exercise 11: Group clusters Exercise 12: Choosing clusters Exercise 13: Cluster sampling analysis

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: Anket analizinde betimsel istatistikler Exercise 2: Frekans dağılımı Exercise 3: Değişkenlik Ölçüleri Exercise 4: Merkezi eğilim ölçüleri Exercise 5: Anket analizinde çıkarımsal istatistikler Exercise 6: Veriyi görselleştir: histogram Exercise 7: z-score'u bul Exercise 8: Korelasyonlar Exercise 9: .corr() ile değişkenleri analiz et Exercise 10: Çalışanlar mutlu mu?Exercise 11: Adil ve açık

Geçerli Egzersiz

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