anova-analysis
There are 46 repositories under anova-analysis topic.
weijie-chen/Basic-Statistics-With-Python
Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.
AliAmini93/Fault-Detection-in-DC-microgrids
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data
This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.
anita-owens/Machine-Learning-R
End-to-end marketing and business analysis projects utilizing machine learning and statistical analysis techniques using the R programming language.
28101991SUNNY/Statistics_study
about statistical techniques for Data Science
DataSpieler12345/python-for-ds-ml
My Python learning experience 📚🖥📳📴💻🖱✏
ashishyadav24092000/Residual-Analysis-in-Linear-Regression
Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot
David-B3/Statistics_Multiple-Mean-Comparison_ANOVA_and_Non-Parametric-Tests
Perform a STEP by STEP multiple mean comparison analysis on R
nalron/project_income_analysis
Projet pour une banque présente dans plusieurs pays, l'objectif est de cibler les prospects les plus susceptibles d'avoir, plus tard dans leur vie, de hauts revenus.
ashishyadav24092000/CAT_SCORE_ANALYSIS_FACTORIAL_EXPERIMENT
This problem concludes which factor is significantly effecting the CAT Score out of College type,program type,and interaction factor type for sample data. Here factorial Experiment design and Two Way Anova is used.
Elliott-dev/Pymaceutical_ANOVA_Tukey
For this Project, I first applied an analysis of variance (ANOVA) model to the Pymaceutical dataset and then did a post-hoc analysis of the results by using Tukey Honest Significant Difference (HSD) to determine which drug treatments in the dataset significantly reduce tumor volume and metastasis. I then wrote a summary of my findings.
nlawira/india-house-rent-prediction
This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.
philsaurabh/Tutorials
Tutorials for BSE classes.
Rae-Zou/Statistics-in-R
A few statistical methods appropriate for applications in the biological and social sciences.
aditiisaxena/Reservoir-Water-Level-Analysis
Econometrics project that aims to analyze the relationship between Reservoir Water Level and various Power Inequalities in different states of India.
alanzanardi/hematological-analysis
Here I have collected two scripts written in Python and SQL, designed for analyzing data related to physiological parameters derived from experimental measurements. These tools were created to expedite the statistical analysis process, extracting and sorting data from tabular-format datasets, in my specific case studies.
AMauGomez/Analisis-Estadistico-de-Datos-vinicolas
Se realiza un análisis estadístico descriptivo de datos obtenidos en 3 viñedos diferentes con el objetivo de encontrar diferencias y relaciones entre las variables medidas, para concluir las características del vino de cada viñedo.
ArpitaShrivas001/Credit_Balance_Analysis
Executed Regression modelling, hypothesis testing and statistical analysis to predict factors affecting credit card balances in a firm. Tools & technologies: ANOVA, p-value, R square
bilnab/P3
projet d'analyse exploratoire et de visualisation
DandiMahendris/regression-model-cac
Regression models for predicting customer acquisition costs (CAC) and the effectiveness of univariate and lasso feature selection techniques in improving the accuracy.
faa-99/R-base
This repository is a code base for R for the purpose of development and code re-use
gaborhor/Happiness-Data-EDA
Introductory-level EDA on UN Happiness Report and World Bank Metrics from 2019
jesschannn/datasci_6_anova
Gain hands-on experience with ANOVA analysis, understanding its assumptions, and applying it to real-world datasets to understand differences among group means.
joycekuohmoukouri/snack
See Readme.md
jxnpass/GlucCog-Final
The data, R programming, and outputs for the research paper testing glucose consumption and cognitive factors. I used R to clean, process, model, and visualize the data. The outputs folder contains the finished products. Link to paper pending.
lucashomuniz/Project-8
Statistical Analysis of Hospitalization Costs: Leveraging SQL and R for Insights
m92vyas/Regression_Analysis_Steel_Offer_Prices
Regression | Analysis | Modelling | Bayesian Search CV | Data Leakage | Overfit/Underfit | End-to-End Project
seherkuutlu/Avila-dataset-analysis-with-python
In this project I used classification algorithms for analysis of avila dataset
SteezieJ/Data-Analytics_Iris_Data
Analytics performed on the fisher iris dataset
vigneshvc99/diabetes_classification
Diabetes Classification Using KNN Model
CS2219/poc_data_analysis
CROWN PROSECUTION SERVICE CASE OUTCOMES BY PRINCIPAL OFFENCE CATEGORY (POC) DATA ANALYSIS AND VISUALIZATION REPORT
pngo1997/Project-Data-Science-Salary-Prediction
Project Data Science Salary Prediction using SAS.
rameshram96/visvaR
Shiny-Based Statistical Solutions for Agricultural Research
Sahar-dev/Multivariate_statistics
Explore multivariate statistics through hands-on university projects. Each project delves into real-world datasets, applying statistical techniques like ANOVA, two-factor analysis, and binary logistic regression. Understand data analysis, interpretation, and modeling with R.