Laboratory works on Elements of Mathematical Statistics course
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Updated
Apr 30, 2018 - Jupyter Notebook
Laboratory works on Elements of Mathematical Statistics course
R file that looks at standard error, confidence intervals, and t-tests in a data set.
Exploring the confidence-Interval concept and bootstrapping.
Analyse efficacy of your own methods for calculating confidence interval
Created a version of the Correlation Matrix in a Diverging Bar Plot form using Stata. This version includes an addition of confidence interval (error bars) with the coefficient.
A repository with my DataCamp Python project practice.
Free WordPress Plugin: This sample size calculator enables you to calculate the minimum sample size and the margin of error. Learn about sample size, the margin of error, & confidence interval. www.calculator.io/sample-size-calculator/
R package to calculate signed root deviance profiles for linear combinations of parameters in a glm. Multiple tests and simultaneous confidence intervals are provided.
Conducted A/B of new ad design
Assignments Basic Stats 1
This repository contains my Inferential Statistics project completed during my university course. It includes the dataset, R code, and detailed analysis showcasing the comparison of climbing route grades for men and women. The project highlights my skills in data visualization, statistical analysis, and hypothesis testing.
Conducted comprehensive data analysis on walmart data, including structure examination, null value detection, and outlier identification in the dataset. Explored and compared average spending patterns among 50 million female and male customers, to derive confidence intervals and provide actionable insights for Walmart's strategic decisions.
Recipe ratio statistical analysis and comparison tool
R functions I made over the years to make my work easier. Includes data visualization functions in base R and functions for extracting relevant info from R model objects to make useful, informative tables.
Inferential Statistics Course 2 in Coursera - Duke University.
Exercise solution o Mathematical Statistic course
Statistical analysis and linear modeling in R
Builds not fully connected neural networks and estimates confidence intervals
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