Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings
#

chi2-contingency

Here are 40 public repositories matching this topic...

Chi2 contengency independence test. Assume Null Hypothesis as Ho: Independence of categorical variables (male-female buyer rations are similar across regions (does not vary and are not related) Thus Alternate Hypothesis as Ha: Dependence of categorical variables (male-female buyer rations are NOT similar across regions (does vary and somewhat/si…

  • Updated Apr 22, 2021
  • Jupyter Notebook

This project uses data to find out why. Through EDA and Chi-square testing, we uncover friction points in daily travel. Key insights: poor infrastructure is the primary driver of extreme delays, and younger generations show a significant, untapped willingness to switch to public transit. Built with Python, Seaborn, and Scikit-Learn.

  • Updated Mar 6, 2026
  • Jupyter Notebook

This project analyzed factors affecting the demand for shared electric cycles in the Indian market. Using EDA and hypothesis testing, I found no significant effect of "working day" on rental count but confirmed that seasonality influences demand. The insights provide valuable guidance for optimizing shared cycle availability.

  • Updated Apr 11, 2025
  • Jupyter Notebook

Chi2 contengency independence test. Q4. TeleCall uses 4 centers around the globe to process customer order forms. They audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. The manager wants to check whether the defective % varies by centre. Please analyze the data a…

  • Updated Apr 23, 2021
  • Jupyter Notebook

Chi2 contengency independence test. Fantaloons Sales managers commented that % of males versus females walking in to the store differ based on day of the week. Analyze the data and determine whether there is evidence at 5 % significance level to support this hypothesis.

  • Updated Apr 24, 2021
  • Jupyter Notebook

Hypothesis-Testing-Chi2-Test-Human-Gender-and-Choice-of-Pets. Assume Null Hypothesis as Ho: Human Gender and choice of pets is independent and not related. Thus Alternate Hypothesis as Ha : Human Gender and choice of pets is dependent and related. As (p_valu=0.1031) > (α = 0.05); Accept Null Hypothesis i.e Independence among categorical variable…

  • Updated May 27, 2021
  • Jupyter Notebook

Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers. Assume Null Hypothesis as Ho: Independence of categorical variables (Athlete and Smoking not related). Thus Alternate Hypothesis as Ha: Dependence of categorical variables (Athlete and Smoking is somewhat/significantly related). As (p_value = 0.00038) < (α = 0.05); Reject Null Hypothesis i.e. De…

  • Updated May 25, 2021
  • Jupyter Notebook

Chi2 contengency independence test image of Buyer Ratio Assume Null Hypothesis as Ho: Independence of categorical variables (male-female buyer rations are similar across regions (does not vary and are not related) Thus Alternate Hypothesis as Ha: Dependence of categorical variables (male-female buyer rations are NOT similar across regions (does v

  • Updated May 5, 2024
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the chi2-contingency topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the chi2-contingency topic, visit your repo's landing page and select "manage topics."

Learn more

Morty Proxy This is a proxified and sanitized view of the page, visit original site.