Menganalisis variabel dengan .corr()

Sebuah survei, birth_survey, dilakukan terhadap 17 bayi untuk menyelidiki keterkaitan antara usia kehamilan saat lahir yang diukur dalam minggu, birth_age, dan berat lahir yang diukur dalam gram, birth_weight.

Dalam latihan ini, Anda akan menghitung hubungan antara usia lahir dan berat lahir.

pandas telah dimuat sebagai pd untuk Anda.

Latihan ini merupakan bagian dari kursus

Menganalisis Data Survei di Python

Instruksi latihan

Gunakan sebuah fungsi untuk menemukan korelasi antara variabel birth_age dan birth_weight dalam survei.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Find the correlation between variables present in the survey
cor = birth_survey.____.____(birth_survey.____)
print(cor)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Menganalisis Data Survei di Python

SkillTag.level.intermediateSkillTag.label

4.7+

Mulai Kursus Gratis

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: Statistik deskriptif dalam analisis survei Exercise 2: Distribusi frekuensi Exercise 3: Ukuran variabilitas Exercise 4: Ukuran tendensi sentral Exercise 5: Statistika inferensial dalam analisis survei Exercise 6: Visualisasikan data: histogram Exercise 7: Cari z-score Exercise 8: Korelasi Exercise 9: Menganalisis variabel dengan .corr()

Latihan Saat Ini

Exercise 10: Apakah karyawan bahagia?Exercise 11: Adil dan sportif

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