normality-test
There are 0 repository under normality-test topic.
majianthu / pycopent
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
causal-discoverycausalitychange-detectionchange-point-detectionchangepointconditional-independence-testconditional-mutual-informationcopulacopula-entropycorrelationentropygranger-causalityhypothesis-testinginformation-theorymutual-informationnormality-testpythontransfer-entropytwo-sample-testvariable-selectionLanguage:Python 158R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
Language:R 41raj-shr-git / Statistical_testing_python
This repository is created for storing the components of Statistical Tests of One Pop, Two Pops and Three or more pops using Python.
Language:Jupyter Notebook 10- normality-tests-pvalues-boxcoxtransformations
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.
Language:Python 6 - Language:Jupyter Notebook 4
IsiashaG / MSDA_Portfolio
Portfolio of Python, SQL, & Tableau while pursing my masters degree in data analytics
Language:Jupyter Notebook 4abhishek-aar-zz / fifaPrediction
A project based on FIFA players dataset from www.kaggale.com
Language:R 2kazilab / XeroGraph
XeroGraph is a Python package developed for researchers and data scientists to analyze, visualize and impute missing data in datasets.
Language:Python 2- Language:Jupyter Notebook 2
YenLinWu / Daily_Work_of_Data_Science
資料科學的日常研究議題
Language:Jupyter Notebook 2- Polygonal-Evolutionary-Algorithm
AlexisBaladon / Polygonal-Evolutionary-Algorithm
Parallel evolutionary algorithm that represents images with polygons using edge detection and denoising. Configuration is carried out through normality tests and pairwise comparisons.
Language:Jupyter Notebook 1 deeprpatel700 / Regression_Analysis_-_Factors_affecting_Life_Expectancy
Statistical Multivariate Regression Analysis to determine the effects of mortality, economic and social factors on life expectancy.
Language:R 1JakubIg / Power_of_normality_tests
Testing the power of normality tests for data simulation
- Language:R 1
MoinDalvs / Learn_Multi_Linear_Regression
Prediction of Miles per gallon (MPG) Using Cars Dataset
Language:Jupyter Notebook 1sdixit5 / Analysis-of-Barack-Obama-s-Presidency
This Project compares the Effective Minimum Wage and Unemployment Rate statistically and Analytically at the start and end of former President Barack Obama's term.
Language:Jupyter Notebook 1tariqshaban / algorithm-comparator
A set of statistical methods conducted on a strict set of algorithm's performance readings, utilizing Python
Language:Python 1boratutumluer / ab-test
:pill: AB Testing Between Versions
Language:Python 0Hermann-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
Language:Python 0JD2112 / ShinyOlink
A Shiny interface to analyse Olink data
Language:R 0KasiMuthuveerappan / FintechCapstone-LoanDefaulters
📗 This repository contains the EDA of loan defaulters, analyzing factors like loan type, ROI, and credit scores. It utilizes Random Forest and XGBoost to clean discrepancies, providing insights to enhance risk assessment and inform lending strategies, making it ideal for financial analysts to mitigate loan default risks.
Language:Jupyter Notebook 0kleki-petra / statistika-testy
Statistics and tests: normal distribution, binomial test, chi2 test, t-test, p-value
Language:R 0SanderBos1 / webAppPyStat
A webapplication that provides a way to calculate statistical functions without having to write any code.
Language:HTML 0shyammanikandan / Walmart_Business_Case_Study
Walmart Customer Purchase behaviour analysis
Language:Jupyter Notebook 0SunJZ2021 / China-GDP-per-Capita-Growth
Research on the reasons for GDP per Capita growth in China
Language:R 0TheDataDesk / ProductInsightEngine
To evaluate different implementations of an in-app currency feature with ABC Analysis
Language:Jupyter Notebook 0ali-unlu / Time-series-analysis-with-SARIMA
In this demonstration, we make a time series analysis based on a temperature data set.
Language:Jupyter Notebookantuniooh / probability-and-statistics-database-analysis
Semester project for the subject of Probability and Statistics, a subject attended at Centro Universitário FEI. It consists of analyzing a database
Language:Jupyter Notebook- Language:Jupyter Notebook
daksh-intwala / Naive-Bayes-Classifier_with_Normality_Tests
Gaussian Navie Bayes Classifier was applied on IRIS dataset. Different types of normality tests were used to introduce the normality concepts.
Language:HTMLEdanur-Y / Variable-Analysis-of-Banks-Ratio-Data
Testing variables for multicollinearity, multivariate normality and analyzing outliers and missing values. ⭕SPSS 🔵R
Jimoh1993 / Data-Science-Customer-Satisfaction-Project
Build a statistical model based customer satisfaction dataset from Qualtrics survey
Language:Rpgplarosa / Understanding-song-characteristics-that-make-it-to-the-top
Applied Computational Statistics Final Project - September 16, 2021
Language:HTMLPriyankaSett / statisticsproblems
This repository contains different statistical methods for Hypothesis testing.
Language:Jupyter Notebookram6ler / RMSE-Normality-Test
A formal normality test based on the RMSE associated with a QQ-plot from a sample.
Language:PythonSuryaVamsi-P / Youtube-Trends-and-Tracks-Engagement
This project aims to leverage Python’s robust visualization libraries to derive deep insights from YouTube video trends data. Using the 'youtube.csv' dataset, this analysis explores the dynamics of YouTube video attributes such as views, likes, dislikes, and comment counts to understand user engagement and their emotional insights.
