Zufällige Stichprobe von Mitarbeitenden

Du bist Analyst:in in einem Unternehmen mit derzeit 500 Mitarbeitenden. Das Unternehmen möchte eine Umfrage durchführen, um die Zufriedenheit der Mitarbeitenden anhand einiger identifizierter Variablen zu ermitteln. Du und das Forschungsteam legt fest, dass die Stichprobe 85 Mitarbeitende umfassen soll. Diese 85 Personen nehmen an der Umfrage teil und dienen als repräsentative Stichprobe für die Gesamtpopulation von 500 Mitarbeitenden.

Der Datensatz und pandas wurden für dich als employee_satisfaction bzw. pd bereitgestellt.

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<Kurs>Umfragedaten in Python analysieren</Kurs>

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# Use n parameter to sample 85 employees
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<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: Zufallsstichprobe Exercise 2: Zufällige Stichprobe von Mitarbeitenden

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Exercise 3: Reproduzierbares Random Sampling Exercise 4: Stratifizierte Zufallsstichprobe Exercise 5: Verteilung von „Yes“ und „No“Exercise 6: Geschichtete Stichprobe Exercise 7: Gewichtete Stichprobe Exercise 8: Blog-Umfrage Exercise 9: Gewichtete Stichprobe zur Händigkeit Exercise 10: Klumpenstichprobe Exercise 11: Cluster gruppieren Exercise 12: Cluster auswählen Exercise 13: Analyse der Klumpenstichprobe

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