Topic: p-value Goto Github
Some thing interesting about p-value
Some thing interesting about p-value
p-value,A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
User: aangelopoulos
p-value,Udacity Data Analyst Nanodegree - Project III
User: abhishek20182
p-value,Correspondence Analysis with python
User: alexianomena
p-value,Minimal A/B Testing Library in PHP
User: andreekeberg
Home Page: https://packagist.org/packages/andreekeberg/abby
p-value,pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets
Organization: bsel-uc3m
p-value,A Convolutional Neural Network Implementation To Classify The Vacancy Of A Parking Spot Using Transfer Learning Methodologies
User: christophersingh
p-value,Generalized linear mixed model elastic net
Organization: debbiemarkslab
p-value,Pearson's Chi-Square Test of Independence for NYHA and KCCQ
User: deboraoliver
p-value,Example of an end to end data analysis project starting from data acquisition to development of insights. Raw python is mostly used.
User: drbashar315
p-value,Simple coin tossing simulation to show the issues with peeking at data during frequentist A/B tests
User: fsalhani
p-value,Analysis of mock A/B Test Results by an e-commerce company. Application of probability, hypothesis testing, sampling distribution, two-sample z-test, and logistic regression to determining whether the company should implement the new web page it developed to increase users' conversion rate
User: jmlcode
p-value,Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.
User: jvirico
p-value,Predicted house prices using multiple linear regression. Used back elimination to further improve the model and select features based on p-value and adjusted R squared value.
User: kaur-anupreet
p-value,Threshold and p-value computations for Position Weight Matrices
User: kchu25
p-value,collection of utility functions for correlation analysis
Organization: kmedian
p-value,Understand the results of an A/B test run by the website and provide statistical and practical interpretation on the test results
User: ksatola
p-value,Analysis platform for large-scale dose-dependent data
Organization: kusterlab
p-value,Hollywood movie analysis to identify the main driver of growth . We used a CSV file with over 45,000 films on which we performed data cleaning, data visualization, and statistical analysis.
User: kwassi09
p-value,GDP Forcasting
User: lastancientone
p-value,Conversion between statistical reporting styles
User: m-damien
Home Page: https://m-damien.github.io/Statslator.js/
p-value,Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers
User: mateuszbuda
Home Page: https://mateuszbuda.github.io/2019/04/30/stat.html
p-value,Shiny Web Application for Making Your p-value Sound Significant
User: nanxstats
Home Page: https://nanx.app/signify/
p-value,Used statistical measures to determine if the rate of re admissions for hospitals are high, if yes, what steps can be taken to bring the rate down.
User: nikhilathota
p-value,E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing
User: nirupamaprv
p-value,First rank winner in the Machine Learning Course Competition for class 2021-2022. Airline ticket price prediction from end to end (analysis - preprocessing - modeling - testing - deployment - documentation) between Indian cities
User: nourkamaly
Home Page: https://airline-ticket-prediction-app.herokuapp.com/
p-value,Adjust p-values for multiple comparisons
User: nunofachada
p-value,Statistical analysis comparing metal accumulation levels in three macroinvertebrate groups
User: odeibarredo
p-value,This repository provides MATLAB implementations of plfit and plpva functions for fitting power-law distributions to empirical data using maximum likelihood estimation (MLE) and statistical goodness-of-fit tests. These tools accurately model complex systems with significant tail behaviors, common in fields like physics, biology, and economics.
User: omarmnfy
p-value,Explore "Statistics" and "Probability Theory" Concepts and Their Implementations in "Python"
User: pegah-ardehkhani
p-value,Multiple hypothesis testing in Python
User: puolival
p-value,Exploring CLT with Python
User: rachel-leigh
p-value,I will include two ways of t tests that compare conversion rate and click through rate of two groups
User: rachelpengmkt
p-value,
User: rebeccak1
p-value,Lean Six Sigma with Python — Kruskal Wallis Test
User: samirsaci
p-value,This repository contains problems on hypothesis testing, confidence intervals, P-value, percentiles, skewness, histogram and more.
User: shahequa
p-value,Analyze ab test results udacity project
User: tantawy997
p-value,A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.
User: usama-tariq
Home Page: https://www.udacity.com/course/data-analyst-nanodegree--nd002
p-value,Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.
User: vaibhavabhimanyoohiwase
p-value,Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.
User: vaitybharati
p-value,Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.
User: vaitybharati
p-value,Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.
User: vaitybharati
p-value,Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak. 1-sample 1-tail ttest. Assume Null Hypothesis Ho as Mean Salmonella <= 0.3. Thus Alternate Hypothesis Ha as Mean Salmonella > 0.3. As No direct code for 1-sample 1-tail ttest available with unknown SD and arrays of means. Hence we find probability using 1-sample 2-tail ttest and divide it by 2 to get 1-tail ttest.
User: vaitybharati
p-value,Hypothesis-Testing-2-Proportion-T-test-Students-Jobs-in-2-States. Assume Null Hypothesis as Ho is p1-p2 = 0 i.e. p1 ≠ p2. Thus Alternate Hypthesis as Ha is p1 = p2. Explanation of bernoulli Binomial RV: np.random.binomial(n=1,p,size) Suppose you perform an experiment with two possible outcomes: either success or failure. Success happens with probability p, while failure happens with probability 1-p. A random variable that takes value 1 in case of success and 0 in case of failure is called a Bernoulli random variable. Here, n = 1, Because you need to check whether it is success or failure one time (Placement or not-placement) (1 trial) p = probability of success size = number of times you will check this (Ex: for 247 students each one time = 247) Explanation of Binomial RV: np.random.binomial(n=1,p,size) (Incase of not a Bernoulli RV, n = number of trials) For egs: check how many times you will get six if you roll a dice 10 times n=10, P=1/6 and size = repetition of experiment 'dice rolled 10 times', say repeated 18 times, then size=18. As (p_value=0.7255) > (α = 0.05); Accept Null Hypothesis i.e. p1 ≠ p2 There is significant differnce in population proportions of state1 and state2 who report that they have been placed immediately after education.
User: vaitybharati
p-value,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. Dependence among categorical variables Thus Athlete and Smoking is somewhat/significantly related.
User: vaitybharati
p-value,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 variables. Thus, there is no relation between Human Gender and Choice of Pets.
User: vaitybharati
p-value,Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.
User: vaitybharati
p-value,Emotion Analysis with Transformers
User: wb-az
p-value,A/B testing using frequentist and Bayesian approaches
User: zuzannna
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