Wähle eine statistische Methode

Wir wollen wissen, ob es eine statistisch signifikante Verbindung zwischen der Lieblingssportart eines Studierenden (Basketball, Baseball, Football) und dem Ort, an dem er oder sie aufgewachsen ist (urban, ländlich), gibt. Um das zu testen, befragen wir zufällig 100 Studierende und fragen sie, in welcher Art von Gegend sie aufgewachsen sind und was ihre Lieblingssportart ist.

Welchen Test verwendest du am besten, um festzustellen, ob es eine statistisch signifikante Verbindung zwischen der Lieblingssportart eines Studierenden und dem Ort, an dem er oder sie aufgewachsen ist, gibt?

Diese Übung ist Teil des Kurses

<Kurs>Umfragedaten in Python analysieren</Kurs>

Interaktive praktische Übung

Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

<Kurs>Umfragedaten in Python analysieren</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.7+

Kurs kostenlos starten

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: Einführung in die Analyse von Umfragedaten Exercise 2: Messniveaus betrachten Exercise 3: Kreuztabelle Exercise 4: Deskriptive und inferenzielle Statistik Exercise 5: Deskriptive Statistik Exercise 6: Inferenzstatistik Exercise 7: Statistische Modellierungstechniken Exercise 8: Streudiagramm prüfen Exercise 9: Wähle eine statistische Methode

Aktuelle Übung

Exercise 10: Stichprobenverfahren zuordnen

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: 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