GitHub topics: p-value
Wb-az/Transformers-Emotion-Analysis 📦
Emotion Analysis with Transformers
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aangelopoulos/ppi_py
A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
Language: Python - Size: 5.89 MB - Last synced at: 2 days ago - Pushed at: 21 days ago - Stars: 240 - Forks: 20
kusterlab/curve_curator
Analysis platform for large-scale dose-dependent data
Language: Python - Size: 47.2 MB - Last synced at: 17 days ago - Pushed at: 17 days ago - Stars: 25 - Forks: 3
mthulin/boot.pval
Bootstrap p-values, including convenience functions for regression models.
Language: R - Size: 1.66 MB - Last synced at: 29 days ago - Pushed at: about 2 months ago - Stars: 4 - Forks: 1
steviecurran/bin-data
Bin data and plot
Language: Python - Size: 161 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0
Pegah-Ardehkhani/Statistics-and-Probability-in-Python
A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python
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josericodata/StatisticsApp
Interactive statistics analysis app using Python and Streamlit. Perform key statistical tests, visualise distributions, and explore data with ease.
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steviecurran/Z-value
Code for calculating Z-score from the p-value
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lingfeiwang/lassopv
Nonparametric P-Value Estimation for Predictors in Lasso
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m-damien/Statslator.js
🔢 Conversion between statistical reporting styles
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puolival/multipy
Multiple hypothesis testing in Python
Language: Python - Size: 1.1 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 105 - Forks: 24
Jayita11/AtliQo-Bank-Credit-Card-Launch-EDA
This project involves exploratory data analysis and statistical testing for AtliQo Bank's new credit card launch. Key insights include targeting high-income occupations and the 18-25 age group. Recommendations focus on tailored marketing campaigns, education, and incentives to enhance credit card adoption and usage among young adults.
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OliverHennhoefer/online-fdr
Online Multiple Hypothesis Testing.
Language: Python - Size: 131 KB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 1 - Forks: 0
nunofachada/pval_adjust
Adjust p-values for multiple comparisons
Language: MATLAB - Size: 6.84 KB - Last synced at: 2 months ago - Pushed at: almost 6 years ago - Stars: 9 - Forks: 2
Torusaynim/Mirea-Data-Analysis
📋 List of practical works from Technologies and Tools for Big Data Analysis subject from university
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LastAncientOne/GDP_Project
GDP Forcasting
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Arjun-08/Effects-of-smoking
This project analyzes the effects of smoking on gene expression, focusing on the interaction between **Smoking Status** and **Gender** using a 2-way ANOVA framework. The results, visualized through a p-value histogram, highlight genes with potential differential responses.
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lmizner/codecademy_farmburg_AB_testing
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lmizner/codecademy_fetchmaker
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lmizner/codecademy_heart_disease
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Loveleen-DS/AB_test
a straightforward approach to assess whether introducing a new landing page leads to an increase in user conversions using Conversion Analysis, One-Tailed T-Test (A/B Test) and Regression Analysis
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Senich17/educational-impact-analysis
A series of analyses to investigate the impact of parental education level on students' math scores.
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andreekeberg/abby
Minimal A/B Testing Library in PHP
Language: PHP - Size: 47.9 KB - Last synced at: 26 days ago - Pushed at: almost 5 years ago - Stars: 32 - Forks: 0
kmedian/korr
collection of utility functions for correlation analysis
Language: Python - Size: 1.77 MB - Last synced at: 25 days ago - Pushed at: almost 3 years ago - Stars: 7 - Forks: 3
yehia55/Analyze-A-B-Test-Results
Applying A/B testing in python
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dnguyennk/Which-Debts-Are-Worth-the-Bank-s-Effort
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
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al1sant0s/Hypothesis-Tests
Python package to perform statistical hypothesis tests.
Language: Python - Size: 713 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
spacebakery/Statistical-Concepts
Statistics for Data Analysis | Sample Mean vs. Population Mean and P-Values
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sohao0819/ab_testing
A collection of ab testing case studies and methodologies
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jeaend/Inferential_Statistic Fork of ta-data-remote/lab-t-tests-p-values
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shwetapardhi/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 t
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shwetapardhi/Assignment-05-Multiple-Linear-Regression-1
Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.p
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shwetapardhi/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.
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shwetapardhi/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 regressi
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shwetapardhi/Assignment-03-Q1--Hypothesis-Testing
Q1.A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validit
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VaibhavAbhimanyooHiwase/Risk_Calculation_using_Backward_Elimination_Algorithm_in_Life_Insurance
Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.
Language: Python - Size: 8.22 MB - Last synced at: 6 months ago - Pushed at: almost 7 years ago - Stars: 12 - Forks: 6
Aichatolba/Graduate-Admission-Logistic-Regression
Logistic regression in R
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dgluesen/ab-testing-trials
Development of the theoretical basis for statistical hypothesis testing with calculation of sample data sets
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Hermann-web/some-common-statistical-methods
python module, showcasing computation (as part of a learning process) of some common statistical methods including mininum sample size, confidence interval estimation methods for mean or proportion, hypothesis testing mehods and regression models witth metrics and test suites
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swiri021/NWPV2
Method of Combined P-value to identify Differential Expressed Gene in dataset
Language: Python - Size: 10.7 KB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
BSEL-UC3M/pMoSS
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
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beery4010/Analyze-AB-Test-Results
Udacity Data Analyst Nanodegree - Project III
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DataSperling/p-values-from-the-null-distribution
An example of how to calculate p-values from real-world experimental data using R and what they mean for statistical inference.
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DerekEgenti/Stats_Comparison_Project
A basic comparison of different statistical methods for data understanding and exploration.
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Mukul-kush/Sun-Pharma-Case-Study
Pharmaceutical company Sun Pharma
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Scrayil/GlobClus_prop-Analysis
This aim of this project is to analyze globular star clusters in the Milky Way, in order to understand their dynamics. The conducted study examined the properties that affect the central velocity dispersion, their impact and the correlations between them.
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samirsaci/lss-kruskal-wallis
Lean Six Sigma with Python — Kruskal Wallis Test
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YannisPap/Test-a-Perceptual-Phenomenon
Analyzed the Stroop effect using descriptive statistics to provide an intuition about the data, and inferential statistics to draw a conclusion based on the results.
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debbiemarkslab/GELMMnet
Generalized linear mixed model elastic net
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NourKamaly/AirlineTicketPricePrediction
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
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seandhan/Insurance-Business-Statistics
Statistical Analysis of Insurance premium data
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pierogio/Hipotesis_Testing
Hypothesis testing on UEFA European leagues dataframe.
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nourhenehanana/Advertising-Means-impact-on-Sales
The advertising dataset captures the sales revenue generated with respect ot advertisement costs across multiple channels loike radio, tv and newspapers. In this little project, we use the linear and multiple regression to understand how spending on advertisements impact sales.
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DeboraOliver/Chi-square
Pearson's Chi-Square Test of Independence for NYHA and KCCQ
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NikhilaThota/Stats_analysis_hospital_readmissions
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.
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NikhilaThota/Stat_analysis_recruiter_calls
Statistical Analysis is used to determine if race plays an important role in callback rates (from recruiters)
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NikhilaThota/Comparing_group_means
Compare the means of two groups (male & female) body temparatures
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vaitybharati/P18.-Hypothesis-Testing-2-Sample-2-Tail-Test-Drugs-and-Placebos-
Hypothesis-Testing-2-Sample-2-Tail-Test-Drugs-and-Placebos. Note: This python code states both 2-sample 1-tail and 2-sample 2-tail codes. Treatment group mean is Mu1 Contrl group mean is Mu2 2-sample 2-tail ttest Assume Null Hypothesis Ho as Mu1 = Mu2 Thus Alternate Hypothesis Ha as Mu1 ≠Mu2.
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vaitybharati/P21.-Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers-
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.
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vaitybharati/P25.-Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data
Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.
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vaitybharati/P17.-Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak-
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.
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vaitybharati/P22.-Hypothesis-Testing-Chi2-Test-Human-Gender-and-Choice-of-Pets-
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.
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vaitybharati/P20.-Hypothesis-Testing-Anova-Test---Iris-Flower-dataset
Hypothesis Testing Anova Test - Iris Flower dataset. Anova ftest statistics: Analysis of varaince between more than 2 samples or columns. Assume Null Hypothesis Ho as No Varaince: All samples population means are same. Thus Alternate Hypothesis Ha as It has Variance: Atleast one population mean is different. As (p_value = 0) < (α = 0.05); Reject Null Hypothesis i.e. Atleast one population mean is different Thus there is variance in more than 2 samples.
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vaitybharati/P16.-Hypothesis-Testing-1S2T---Call-Center-Process
Hypothesis Testing 1S2T - Call Center Process. Sample Parameters: n=50, df=50-1=49, Mean1=4, SD1=3 1-sample 2-tail ttest Assume Null Hypothesis Ho as Mean1 = 4 Thus, Alternate Hypothesis Ha as Mean1 ≠4
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vaitybharati/P19.-Hypothesis-Testing-2-Proportion-T-test-Students-Jobs-in-2-States-
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.
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vaitybharati/P15.-Hypothesis-Testing-1S1T---Super-Market-Loyality-Program
Hypothesis-Testing 1S1T-Super-Market-Loyality-Program. Population Parameters: Mean=120 Sample Parameters: n=80, Mean=130, SD=40, df=80-1=79
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vaitybharati/Assignment-05-Multiple-Linear-Regression-2
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.
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vaitybharati/Assignment-04-Simple-Linear-Regression-1
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.
Language: Jupyter Notebook - Size: 43 KB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 6
odeibarredo/Statistics_Group-Comparisons
Statistical analysis comparing metal accumulation levels in three macroinvertebrate groups
Language: R - Size: 687 KB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 2 - Forks: 1
RRafiee/Association
Finding association between clinical, pathological and molecular features
Language: R - Size: 436 KB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 1 - Forks: 0
rebeccak1/pricing-test
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mateuszbuda/ml-stat-util
Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers
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olesyamba/ICvsML
Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.
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vaitybharati/Assignment-04-Simple-Linear-Regression-2
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.
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christophersingh/Car-CNN-Capstone
A Convolutional Neural Network Implementation To Classify The Vacancy Of A Parking Spot Using Transfer Learning Methodologies
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YujiSODE/draw2Sample 📦
the interface for graphical sampling in order to generate values following an empirical distribution, with img/canvas tag on Firefox.
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saramostafaali/Analyze-A-B-Test-Results
My second project from Udacity's data analysis advanced track, provided by FWD scholarship.
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nirupamaprv/Analyze-AB-test-Results
E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing
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zuzannna/AB-Testing-Bayes
A/B testing using frequentist and Bayesian approaches
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guhjy/sim_ES-inflation-by-pub-bias Fork of amscheel/sim_ES-inflation-by-pub-bias
This is an R script for a minimal simulation of the influence of extreme publication bias (only significant results get published) on effect sizes.
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guhjy/sgpvalue Fork of LucyMcGowan/sgpvalue
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guhjy/PPV-NPV-FDR-FOR_plot Fork of amscheel/PPV-NPV-FDR-FOR_plot
R code to plot the positive and negative predictive for different levels of power across the whole range of % true hypotheses tested.
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guhjy/power_f-alpha- Fork of amscheel/power_f-alpha-
R code to plot power as a function of alpha
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guhjy/p-checker Fork of nicebread/p-checker
p-checker: The one-for-all p-value analyzer. R-Index, p-curve, and more in one online app.
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guhjy/justify-alpha Fork of allefeld/justify-alpha
trying to justify my alpha
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guhjy/blog-1 Fork of GRousselet/blog
code and data for posts from https://garstats.wordpress.com
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guhjy/BF-vs-p Fork of amscheel/BF-vs-p
A quick investigation on why Bayes factors and p-values are correlated and why p-values are uniformly distributed under H0
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guhjy/Alt-NHST Fork of doomlab/Alt-NHST
Alternatives to NHST paper
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guhjy/2014tgtbf Fork of chartgerink/2014tgtbf
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nanxstats/signify
Shiny Web Application for Making Your p-value Sound Significant
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lingfeiwang/fdrtoolw
Estimate (Local) False Discovery Rates with Weights, adapted from fdrtool
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Abanoub8yuossef/Deleted
using hypothesis testing and regression to analyze data about the new feature in the application
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deepdhungel/Charite-Projects-2017
MyWork
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S-CHAN11/Salary-Variance-Analysis
In this project I analyze if the salary of employees is dependent on their education or occupation using Advanced Statistical concepts like Hypothesis Testing and Anova
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AlaaNabil98/Analyze-A-B-Test-Results
working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.
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ZofiaQlt/customer_behaviour
🎯 Customer behaviour and sales analysis for a national bookstore library willing to adapt its digital vs. instore strategy - use of Python and JupyterLab (Business insights, Data collection, Cleaning, EDA, Market Segmentation, Time Series analysis, Statistical tests and Data Visualization)
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jvirico/normality-tests-pvalues-boxcoxtransformations
Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.
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KWASSI09/hollywood_vc
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.
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RichmondDjwerter/Mobile-Games-A-B-Testing-with-Cookie-Cats
Cookie Cats is a hugely popular mobile puzzle game, it's a classic "connect three"-style puzzle game. in this notebook we're going to analyze an AB-test where we moved the first gate in Cookie Cats from level 30 to level 40. In particular, we will look at the impact on player retention.
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Usama-Tariq/-Udacity_Analyze-A-B-Test-Results_Project-3_DAND
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.
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