Tabelle a doppia entrata

Studenti e alumni, principalmente dell'University of Texas, sono stati intervistati per conoscere la loro posizione sul tema dell'empowerment femminile. Facciamo un primo quadro della relazione tra titolo di studio ottenuto e genere.

pandas è già stato importato come pd e il sondaggio come we_survey.

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Analisi dei dati da sondaggio in Python

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Istruzioni dell'esercizio

Crea una tabella a doppia entrata tra Gender e Education che mostri la distribuzione di frequenza tra entrambe le variabili.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a cross-tabulation of "Gender" and "Education"
cross_tabulation = ____.____(we_survey.____,
                             we_survey.____, 
                             margins = True)
print(cross_tabulation)

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Questo esercizio fa parte del corso

Analisi dei dati da sondaggio in Python

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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: Introduzione all'analisi dei dati da sondaggi Exercise 2: Esaminare i livelli di misurazione Exercise 3: Tabelle a doppia entrata

Esercizio in corso

Exercise 4: Statistica descrittiva e inferenziale Exercise 5: Statistiche descrittive Exercise 6: Statistica inferenziale Exercise 7: Tecniche di modellazione statistica Exercise 8: Ispezione con scatter plot Exercise 9: Scegli un metodo statistico Exercise 10: Abbina la tecnica di campionamento

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