Examiner les niveaux de mesure

Un analyste souhaite étudier l’impact des animaux de compagnie sur le stress. Les propriétaires actuels d’animaux ont été interrogés sur leur premier animal, first_pet, le nombre d’animaux qu’ils ont actuellement, current_pets, le temps qu’ils passent avec leurs animaux, time_spent, et s’ils estiment qu’avoir un animal réduit le stress, reduces_stress.

Quel serait le niveau de mesure de la variable first_pet ?

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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: Introduction à l’analyse des données d’enquête Exercise 2: Examiner les niveaux de mesure

Exercice actuel

Exercise 3: Table de contingence Exercise 4: Statistiques descriptives et inférentielles Exercise 5: Statistiques descriptives Exercise 6: Statistiques inférentielles Exercise 7: Techniques de modélisation statistique Exercise 8: Inspection d’un nuage de points Exercise 9: Choisir une méthode statistique Exercise 10: Faire correspondre la technique d’échantillonnage

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