Question: COMM 310 - Final Report Description Overview: This is a two-part lab, and part 2 asks you to analyze data from two separate studies. The

COMM 310 - Final Report Description

Overview:This is a two-part lab, and part 2 asks you to analyze data from two separate studies. The specific questions that you need to answer are numbered and bolded. When you turn in this lab, please ensure that your responses follow the numbered questions and that you have addressed every question posed.

Part 1: A/B Testing

For part 1, you will need analyze the dataset "ABtestingLabData" using Microsoft Excel.

An e-commerce website decided to conduct an A/B test to choose between two different versions of its landing page. Users coming to the site were assigned at random in equal numbers to one of the two pages, and their responses were monitored. A total of 100 users were tested but, due to technical issues, the number of users exposed to each version was slightly different. Two KPIs were collected:

  • conversions (if they placed an order or not)
  • the dollar value of transactions

The data are reported in the attached spreadsheet.

Based on these results, your task is to determine which version of the landing page is more effective. In doing so, please answer each of the following questions:

  1. Compare the raw counts of each version and present them in a table. Which version appears to have done better, based on these results?

Converted

Did not convert

Total cases

Version A

Version B

Total (#)

Percentage (%)

  1. Conduct a chi-square test of comparison between Version A and Version B. Compare the means of each version. With this raw comparison, which version appears to be better? Consider the standard deviation, an indication of how spread out the distribution is. If the distributions overlap significantly, it is less likely that there are real differences between the samples. If the standard deviation is large compared to the averages, further testing might be needed to compare the means.

# of orders

Total order value ($)

AOV = total/# ($)

Standard deviation

Version A

Version B

  1. Conduct a t-test and report the appropriate values. What can we learn from this t-test?

Converted? Order Value
[Yes=1, No=0] [$]
version A 0 -
version A 0 -
version A 0 -
version A 1 5.14
version A 0 -
version A 0 -
version A 0 -
version A 1 4.64
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 1 45.43
version A 0 -
version A 0 -
version A 1 33.50
version A 1 31.41
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 1 36.60
version A 0 -
version A 0 -
version A 0 -
version A 1 54.31
version A 1 21.85
version A 0 -
version A 1 44.80
version A 1 42.80
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 1 1.00
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 0 -
version A 1 2.43
version B 1 28.01
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 1 42.77
version B 1 2.41
version B 0 -
version B 0 -
version B 0 -
version B 1 12.75
version B 1 20.87
version B 0 -
version B 0 -
version B 1 38.68
version B 0 -
version B 0 -
version B 1 47.29
version B 1 51.10
version B 1 82.90
version B 0 -
version B 1 39.92
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 0 -
version B 1 44.04
version B 1 26.43
version B 1 29.23
version B 1 16.96
version B 0 -
version B 0 -
version B 0 -

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