R Package for Bootstrap Unit Root Tests
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Updated
May 20, 2024 - R
R Package for Bootstrap Unit Root Tests
Grubbs' test for outliers.
R6-Based Flexible Framework for Permutation Tests
Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.
Comprehensive statistical testing analysis with ecommerce data.
Examples of generating random variables from the basic probability distributions
Aider une entreprise agro-alimentaire de réaliser une étude de marché international pour mieux cibler ses nouveaux pays clientèle.
This is a project about analyzing tuberculosis data from 1990 to 2013 using Tableau.
This is the first exercise in the data science course at Shahid Beheshti University. In this exercise, we will analyze and review data as well as statistical tests.
Testing hypothesis and building Confidence Intervals
This is the first exercise in the data science course at Shahid Beheshti University. In this exercise, we will analyze and review data as well as statistical tests.
Moving Grubbs' test for outliers.
a spark extensions to help analyze abtest experiments based on raw data
A hospital wants to determine whether there is any difference in the average Turn Around Time (TAT) of reports of the laboratories on their preferred list. They collected a random sample and recorded TAT for reports of 4 laboratories. TAT is defined as sample collected to report dispatch. Analyze the data and determine whether there is any diff…
Two-Dimensional Data Whitening and Receiver Operational Characteristic Determination
Matéria de Análise Estatística de Dados da pós-graduação em Ciência de Dados e Machine Learning
Statistical analysis on online retail analysis
This is an application statistical hypothesis testing on data from a Datacamp competition
A short study on four marketing channels of Divar company
This project investigates whether there is a statistically significant difference in the average climbing route grades between males and females. Using R, the analysis employs both traditional (t-test) and non-traditional (bootstrap and permutation methods) inferential statistics to test the hypothesis.
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