Visualizza i dati: istogramma

Per il progetto del tuo corso di statistica, hai chiesto ai tuoi amici di partecipare a un sondaggio per comprendere meglio le differenze di genere in alcune preferenze tra i giovani adulti. Una parte del sondaggio, young_people, ha raccolto Gender, Age, Height e Weight dei partecipanti.

In questo esercizio visualizzerai la distribuzione dell’altezza dei rispondenti.

pandas, scipy.stats e matplotlib.pyplot sono già stati caricati per te come pd, stats e plt.

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Analisi dei dati da sondaggio in Python

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Istruzioni dell'esercizio

Rappresenta la distribuzione dei valori nella colonna Height usando un istogramma.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Histogram of Height column
young_people.____.____(kind=____)
plt.show()

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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: 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: Statistiche descrittive nell'analisi dei sondaggi Exercise 2: Distribuzione di frequenza Exercise 3: Misure di variabilità Exercise 4: Misure di tendenza centrale Exercise 5: Statistiche inferenziali nell'analisi dei sondaggi Exercise 6: Visualizza i dati: istogramma

Esercizio in corso

Exercise 7: Calcola lo z-score Exercise 8: Correlazioni Exercise 9: Analizza le variabili con .corr()Exercise 10: I dipendenti sono felici?Exercise 11: Vincere a testa alta

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