Using boxplots to investigate US hospitals healthcare costs
-
Updated
Feb 7, 2023 - Jupyter Notebook
Using boxplots to investigate US hospitals healthcare costs
collection of Jupyter Notebooks in both English and Spanish, dedicated to performing data quality analysis using the R programming language
The Following problems showcase different Statistical Methods used for Decision Making. The purpose of this project is to experiment and execute statistical methods, which are required to conduct data analysis, derive insights and inferences and arrive at business decisions.
This repository includes all the assignments completed for the IDS702: Modelling & Representation of Data at Duke MIDS program.
Using pandas and numpy to explore London weather data to find the best time to visit.
Using matplotlib to look at distributions of flowers and flights to plan a trip.
This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets.
Statistics for Data Science Assignment
Statistics for Data Science Hackathon
This repository contains a gentle introduction to machine learning algorithms with hands on practical examples
Statistics for Data Science Assignment
WHO LIFE EXPECTANCY: Studying the factors that affect/contribute to life expectancy and analyzing the changes over the last 15years, that is between 2000-2015.
A simple data science project/hackathon done as part of SDS course
Statistics for Data Science Assignment
Excel calculating the probability distribution simulated data
A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.
Book of Demythologize Durbin-Watson Test Statistic | Correct the critical values of DW statistic
The R code authored below goes through cleaning, visualizing and modeling data as well as some useful simulations for concepts in Research Statistics and markdown reports. Some code is shell code for the participant to complete; some are examples of the completed shell code. For more advanced R methods, see Dashboards_DataScience repo
Learn the core statistical concepts, followed by application of these concepts using R Studio with the a nice combination of theory and practice. Learn key statistical concepts and techniques like exploratory data analysis, correlation, regression, and inference.
Add a description, image, and links to the statistics-for-data-science topic page so that developers can more easily learn about it.
To associate your repository with the statistics-for-data-science topic, visit your repo's landing page and select "manage topics."