Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

Segregation Measurement, Inferential Statistics, and Decomposition Analysis

Python Inferential Statistics/ Linear & Logistic Regression Tutorials

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

Data Wrangling, Linear Models & other misc. Inferential Statistics.

Visualise the Results of Inferential Statistics using 'ggplot2'

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The code repository for the talk delivered on Inferential Statistics at SciPy India 2017

Decision Tree for Inferential Statistics

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This is a repository created in line with my understanding & implementation of the major complex ideas in Machine Learning & Inferential Statistics while working as a data science professional in the industry.

General Social Survey data analysis project for Inferential Statistics Coursera-Duke course.

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Analyzed CMSU student data, shingle moisture levels, and salary by education and occupation using inferential statistics. Applied probability, t-tests, and ANOVA to uncover insights on demographics, product quality, and salary trends. Key findings support data-driven decisions.

This repository contains activities and lectures notes from the Coursera Inferential Statistics course, which is part of the Statistics with R specialization.

Python package containing functions implemented for descriptive and inferential statistics.

Elementary Statistics course that covers descriptive statistics, regression, probability and inferential statistics (confidence intervals and parametric test hypothesis).

Using inferential statistics to explore the GSS dataset.

Estimation for SPSS - An extension bundle that better supports the estimation approach to inferential statistics within SPSS

A web-based statistics calculator for the System Usability Scale providing inferential statistic calculation utility for hypothesis testing.

This course is an introduction to probability and statistics. Main goal is to teach students how to apply various statistical concepts to decision making under uncertainty. After this course, students should be able to analyze data and relationships among variables using both descriptive and inferential statistics, and to evaluate and update decision alternatives so that they can find solutions to real life business problems.

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HW solutions to Inferential Statistics course on Coursera taught by professors at Duke University

Inferential Statistics Project

Learn how to use R, conduct inferential statistics, and use classical test theory.

Contains inferential statistical practices for machine learning models and analyses. Using Python and developing statistical thinking to work with a limited sample of data and be able to generate predictions about it. Applying confidence intervals to estimate unknown values. Using bootstrapping to simulate data acquisition repeatedly. Development of hypotheses of their models. Sampling of populations to facilitate analysis.

Boston House Dataset: descriptive and inferential statistics, and prediction of the variable price using keras to create a neural network.

This repository has been created for storing the Python code for building the understanding of Descriptive and Inferential Statistics and other supporting components.

Slide for ASA miniconference talk "What Inferential Statistics Can Utilize from Predictive Models"