Statistical Analysis Challenge Week 1
Welcome to the statistical analysis challenge for this week! You will assume the role of a data scientist for an e-commerce company in this fascinating task as you assess the effects of a recent website redesign. The business is interested in knowing whether user engagement has improved as a result of their new design. Your goal is to utilize your data science expertise to analyze and evaluate the situation.
Various Practice Areas:
Testing Hypotheses: To ascertain whether the redesign has resulted in any significant changes, you will formulate and test hypotheses. This is a crucial ability in the field of data science because it enables us to make defensible decisions using sample data.
Statistical Analysis: The t-test and p-values must be understood to successfully complete this challenge. The means of two independent groups will be compared using a one-tailed t-test. This enables us to assess whether group differences are statistically significant.
Data Interpretation: After completing the test, you must consider the results in light of the situation that would actually occur in real life. This includes comprehending the meaning of the p-value and t-statistic in relation to the current business question.
Python coding: To perform the t-test, you’ll use Python libraries like SciPy. This task will give you practical coding experience and deepen your understanding of how to use Python for statistical analysis.
This challenge will test both your technical knowledge and your ability to use these skills in a practical setting. It combines statistical rigor with real-world applicability. It’s a fantastic chance to observe how data science methods can influence business decision-making.
Suppose you are a data scientist working for an e-commerce company that has recently launched a new website design. The company wants to know if the new design has led to a significant increase in user engagement, measured by the average time spent on the site.
You are given two sets of data: the average time users spent on the site before the design change (Group A) and the average time users spent on the site after the design change (Group B).
Your task is to perform a hypothesis test to determine if there is a statistically significant difference between the two groups.
- Null Hypothesis: The mean time spent on the site before and after the redesign is the same.
- Alternative Hypothesis: The mean time spent on the site after the redesign is greater than before.
Here are two lists that contain the data for groups A and B.
group_A = [5.2, 4.8, 5.0, 5.1, 4.9, 5.0, 5.3, 5.1, 5.2, 5.0]
group_B = [5.5, 5.7, 5.9, 5.6, 5.7, 5.8, 6.0, 5.9, 5.7, 5.6]
Those who submit an entry will be eligible to win a free copy of any book of their choice by Packt! The more weeks you participate, the more chances you get to win! To submit your entries, make a LinkedIn post with a screenshot of your answers. You can explain your thought process if you’d like. Just make sure to tag the Data in Motion LLC company page.