Campione casuale di dipendenti

Sei un analista in un'azienda che attualmente impiega 500 persone. L'azienda vuole condurre un sondaggio per valutare la soddisfazione dei dipendenti in base ad alcune variabili individuate. Tu e il team di ricerca decidete di fissare il campione a 85 dipendenti. Questi 85 dipendenti parteciperanno al sondaggio e rappresenteranno l'intera popolazione di 500 dipendenti.

Il dataset e pandas sono già stati caricati come employee_satisfaction e pd, rispettivamente.

Questo esercizio fa parte del corso

Analisi dei dati da sondaggio in Python

Visualizza il corso

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Use n parameter to sample 85 employees
____

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi dei dati da sondaggio in Python

IntermediárioNível de habilidade

4.7+

Inizia il corso gratis

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: Campionamento casuale Exercise 2: Campione casuale di dipendenti

Esercizio in corso

Exercise 3: Campionamento casuale riproducibile Exercise 4: Campionamento casuale stratificato Exercise 5: Distribuzione di "Yes" e "No"Exercise 6: Campionamento stratificato Exercise 7: Campionamento ponderato Exercise 8: Sondaggio sul blog Exercise 9: Campionamento pesato sulla lateralità Exercise 10: Campionamento a grappolo Exercise 11: Raggruppa i cluster Exercise 12: Scelta dei cluster Exercise 13: Analisi del campionamento a grappolo

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