Statistical tests when the sample size is small/outliers are present/sample does not follow a normal distribution with an example in R.
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Updated
Jun 7, 2024 - R
Statistical tests when the sample size is small/outliers are present/sample does not follow a normal distribution with an example in R.
R Shiny app for differential-expression analysis using T-test. The app is automatically deployed via GH Actions to shinyapp.io available as https://fuzzylife.shinyapps.io/diffExpr/
A statistical analysis of production data on a prototype vehicle using R.
Using R and statistics in RStudio to analyze different variables for review. Use the production data for insights that may help the manufacturing team. Perform multiple linear regression analysis to identify which variables in the dataset predict the mpg .
Using R to review production data for factors affecting vehicle performance
Analysis of MechaCar dataset to establish relationships between features and miles per gallon(mpg) on a variety of cars. Various types of statistical analysis were performed using RStudio to establish potential relationships and the accuracy of each test respectively.
Statistical Analysis with R - Summary Statistics, T-Tests, ANOVA
MechaCar prototypes Collected summary statistics on the pounds per square inch (PSI) of the suspension coils from the manufacturing lots Ran t-tests to determine if the manufacturing lots are statistically different from the mean population Designed a statistical study to compare vehicle performance of the MechaCar vehicles against vehicles from…
Project in R Language, work on datasets performing Statistics analysis
This project uses R for a statistical analysis of car data to predict performance using multiple metrics
Simulation-based proofs (CLT, LLN, ...) & Statistical Tests (Difference in Means, Chi-Squared, ANOVA)
Performing multiple regression to predict fuel efficiency (MPG) on an energy-efficient car model
An R project that conducts a hypothesis test to analyze the validity of Robin Dunbar's claim that humans only have the capacity to keep track of a maximum of 150 people (Dunbar's Number) at a time. Through this project, Dunbar's hypothesis is investigated with the use of multiple t-tests conducted over a Facebook usage dataset.
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