AparajitaMukherjee/Evaluated-the-Impact-of-a-New-Golf-Ball-Coating-Hypothesis-Testing-Statistical-Inference- AparajitaMukherjee/Evaluated-the-Impact-of-a-New-Golf-Ball-Coating-Hypothesis-Testing-Statistical-Inference-

  • Stars
    star
    1
  • Language
  • Created about 4 years ago
  • Updated about 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
AparajitaMukherjee/Evaluated-the-Impact-of-a-New-Golf-Ball-Coating-Hypothesis-Testing-Statistical-Inference-
github

AparajitaMukherjee

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This project used Hypothesis Testing and visualization to evaluate a new golf ball coating designed to resist cuts and provide durability by comparing the driving distances of the old and new golf ball.

More Repositories

1

Credit-Risk-Modeling-ARIMA-Time-Series-Forecasting-ADF-Test

ARIMA, Time Series Forecasting, ADF Test
R
1
star
2

Prediction-of-Diabetes-in-Women-Logistic-Regression-Model-Comparison-Predictive-Analytics-

The primary objective was to predict the occurrence of Diabetes in Women, given the personal details of a female. A Logistic Regression Model was developed and validated with test data to predict the chances of someone suffering from the disease.
R
1
star
3

Customer-Satisfaction-ANOVA-Linear-Regression-Factor-Analysis-

The objective of the project is to use the dataset Factor-Hair-Revised.csv to build a regression model to predict satisfaction. Project Approach: Data Exploration, Collinearity of the variables, Initial Regression analysis, Factor Analysis, Labelling and interpreting of the factors. Regression analysis using the factors as independent variable. Model performance measures.
1
star
4

Building-a-Supervised-Model-to-cross-sell-Personal-Loans-Random-Forest-Data-Mining-Pruning-Model-

The objective of this exercise was to build a model using a Supervised learning technique to figure out profitable segments to target for cross-selling personal loans. A Pilot campaign data of 20000 customers was used which included several demographic and behavioral variables. The Model was further validated and a deployment strategy was recommended.
R
1
star