paired-t-test
There are 0 repository under paired-t-test topic.
- from-sas-to-python
aidanastridge / from-sas-to-python
All of these previous analyses were done in SAS. I transitioned them over to Python to practice the language.
Language:Jupyter Notebook 2 Allen-Ho-0302 / Contract-Year-Phenomenon
Is it true that players tend to perform better in their contract years? Furthermore, can teams or fantasy managers really take advantage of it?
SourajitaDewasi / StasticalDataAnalysis
Visualization from Statistical Data Analysis
Language:Jupyter Notebook 1acaturan / Caturan-BIMM-143-Project-2-Final
This repository consists of an R Notebook utilizing the following bioinformatics methods: p-values and Deep Neural Networks. Histograms have been used to plot the differences between before training and after training results. The csv file "BIMM 143 Project 2.csv" will be necessary for code to run.
0DerekEgenti / Stats_Comparison_Project
A basic comparison of different statistical methods for data understanding and exploration.
Language:Jupyter Notebook 0faisal-irzal / paired_sample_t_test
This repository will explain about paired sample t-test, the test procedure and how to use it using Scipy library of Python.
Language:Jupyter Notebook 0juliorodrigues07 / ny_rent_pricing
Rent pricing prediction on NY properties with interactive dashboards.
data-visualization-dashboardexploratory-data-analysisfeature-selectionfine-tuningmachine-learningregression-modelsdata-miningkernel-density-estimationlearning-curvespcarent-predictionstudent-projectrecursive-feature-elimination-cvregression-metricsremoving-outliersfeature-importancepaired-t-testpreprocessing-datastreamlit-applicationstreamlit-dashboardLanguage:Jupyter Notebook 0nirupamaprv / Statistics
Hypothesis testing and Statistical Analysis Projects
Language:HTML 0UdeshikaDissa / Machine-Learning_Supervised-Learning
Predicting the Contraceptive Method Choice of a Woman Based on Demographic and Socio-economic Characteristics - The objective of this study is to to predict the contraceptive methods (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. A data-set of 1473 married women with their demographic and socio-economic characteristics used in this study. The Source for the data-set is the UCI Machine Learning Repository at, http://http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice [?]. This study consists of two phases. The objective of Phase I is to preprocess and explore the data-set in order to build the model in Phase II. All the activities have been performed in the Python package in this study and Compiled from Jupyter Notebook This report covers both narratives and the Python pseudocodes for the data preprocessing and exploration performed under phase I. Content of this report is organized as follows. Section 1 describes the data sets and their attributes. Section 2 covers data preprocessing. In Section 3, each attribute and its inter-relationships are explored.
Language:Jupyter Notebook 0- Language:Jupyter Notebook 0
joydowns / Cigarette
Project in R analyzing "Cigarette" dataset using libraries Ecdat, ggplot2, dplyr
khlinh2512 / BusinessAnalytics
Machine Learning Algorithms Collection
Language:Jupyter NotebookKwokHing / Pencils-of-Promise
Data for A Cause - Pencils of Promise
PriyankaSett / statisticsproblems
This repository contains different statistical methods for Hypothesis testing.
Language:Jupyter Notebook
