Variablen mit .corr() analysieren

Eine Umfrage, birth_survey, wurde mit 17 Säuglingen durchgeführt, um den Zusammenhang zwischen dem Schwangerschaftsalter bei der Geburt in Wochen, birth_age, und dem Geburtsgewicht in Gramm, birth_weight, zu untersuchen.

In dieser Übung berechnest du die Beziehung zwischen birth_age und birth_weight.

pandas wurde als pd für dich geladen.

Diese Übung ist Teil des Kurses

<Kurs>Umfragedaten in Python analysieren</Kurs>

Übungsanweisungen

Verwende eine Funktion, um die Korrelation zwischen den Variablen birth_age und birth_weight in der Umfrage zu bestimmen.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

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

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Umfragedaten in Python analysieren</Kurs>

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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: Deskriptive Statistik in der Umfrageanalyse Exercise 2: Häufigkeitsverteilung Exercise 3: Streuungsmaße Exercise 4: Lagemaße der zentralen Tendenz Exercise 5: Inferenzstatistik in der Umfrageanalyse Exercise 6: Daten visualisieren: Histogramm Exercise 7: Finde den z-Score Exercise 8: Korrelationen Exercise 9: Variablen mit .corr() analysieren

Aktuelle Übung

Exercise 10: Sind die Mitarbeitenden glücklich?Exercise 11: Fair und gerecht

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