Calcolare gli intervalli di confidenza

Ora calcolerai gli intervalli di confidenza per i risultati dell'A/B test.

I quattro valori calcolati in precedenza sono già stati caricati per te (cont_conv, test_conv, test_size, cont_size) come variabili con quei nomi.

Questo esercizio fa parte del corso

Customer Analytics and A/B Testing in Python

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Istruzioni dell'esercizio

Calcola la media della distribuzione del lift sottraendo cont_conv da test_conv.
Calcola la varianza della nostra distribuzione del lift completando il calcolo. Devi completare la parte relativa al gruppo di controllo della varianza.
Trova la deviazione standard della distribuzione del lift facendo la radice quadrata di lift_variance.
Trova gli estremi di confidenza per il nostro A/B test con valore uguale a lift_mean, livello di confidenza 0.95 e lift_sd calcolata. Passa gli argomenti in quell'ordine.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Calculate the mean of our lift distribution 
lift_mean = ____ - ____ 

# Calculate variance and standard deviation 
lift_variance = (1 - test_conv) * test_conv /test_size + (____ - ____) * ____ / ____
lift_sd = ____**0.5

# Find the confidence intervals with cl = 0.95
confidence_interval = get_ci(____, ____, ____)
print(confidence_interval)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Customer Analytics and A/B Testing in Python

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This chapter provides a brief introduction to the content that will be covered throughout the course before transitioning into a discussion of Key Performance Indicators or KPIs. You'll learn how to identify and define meaningful KPIs through a combination of critical thinking and leveraging Python tools. These techniques are all presented in a highly practical and generalizable way. Ultimately these topics serve as the core foundation for the A/B testing discussion that follows.

Exercise 1: Course introduction and overview Exercise 2: Understanding the key components of an A/B test Exercise 3: Defining meaningful KPIs Exercise 4: Identifying and understanding KPIs Exercise 5: Loading & examining our data Exercise 6: Merging on different sets of fields Exercise 7: Exploratory analysis of KPIs Exercise 8: Practicing aggregations Exercise 9: Grouping & aggregating Exercise 10: Calculating KPIs - a practical example Exercise 11: Calculating KPIs Exercise 12: Average purchase price by cohort

This chapter teaches you how to visualize, manipulate, and explore KPIs as they change over time. Through a variety of examples, you'll learn how to work with datetime objects to calculate metrics per unit time. Then we move to the techniques for how to graph different segments of data, and apply various smoothing functions to reveal hidden trends. Finally we walk through a complete example of how to pinpoint issues through exploratory data analysis of customer data. Throughout this chapter various functions are introduced and explained in a highly generalizable way.

Exercise 1: Working with time series data in pandas Exercise 2: Parsing dates Exercise 3: Creating time series graphs with Matplotlib Exercise 4: Plotting time series data Exercise 5: Pivoting our data Exercise 6: Examining the different cohorts Exercise 7: Understanding and visualizing trends Exercise 8: Seasonality and moving averages Exercise 9: Exponential rolling average & over/under smoothing Exercise 10: Events and releases Exercise 11: Visualizing user spending Exercise 12: Looking more closely at revenue

In this chapter you will dive fully into A/B testing. You will learn the mathematics and knowledge needed to design and successfully plan an A/B test from determining an experimental unit to finding how large a sample size is needed. Accompanying this will be an introduction to the functions and code needed to calculate the various quantities associated with a statistical test of this type.

Exercise 1: Introduction to A/B testing Exercise 2: Good applications of A/B testing Exercise 3: General properties of an A/B Test Exercise 4: A/B test generalizability Exercise 5: Initial A/B test design Exercise 6: Experimental units: Revenue per user day Exercise 7: Preparing to run an A/B test Exercise 8: Conversion rate sensitivities Exercise 9: Sensitivity Exercise 10: Standard error Exercise 11: Calculating sample size Exercise 12: Exploring the power calculation Exercise 13: Calculating the sample size

After running an A/B test, you must analyze the data and then effectively communicate the results. This chapter begins by interleaving the theory of statistical significance and confidence intervals with the tools you need to calculate them yourself from the data. Next we discuss how to effectively visualize and communicate these results. This chapter is the culmination of all the knowledge built over the entire course.

Exercise 1: Analizzare i risultati dell'A/B test Exercise 2: Confermare i risultati del test Exercise 3: Ragionare in modo critico sui p-value Exercise 4: Capire la significatività statistica Exercise 5: Intuizione dietro la significatività statistica Exercise 6: Verificare la significatività statistica Exercise 7: Capire gli intervalli di confidenza Exercise 8: Calcolare gli intervalli di confidenza

Esercizio in corso

Exercise 9: Interpretare i risultati del test Exercise 10: Rappresentare la distribuzione Exercise 11: Tracciare la distribuzione delle differenze Exercise 12: Finale