Cover Image

Statistical analysis with Python.

Using examples, Dr. Chirag Shah, PhD, illustrates statistical analysis with Python using NumPy to generate coefficients, pandas to load data frames, matplotlib.pyplot to create graphs, and statsmodel.api to build linear regression models.

Bibliographic Details
Other Authors:	Shah, Chirag (Speaker)
Format:	Video
Language:	English
Language Notes:	Closed-captions in English.
Published:	Somerset : Chirag Shah, 2018.
Series:	Introduction to Data Science with Python ; 3.
Subjects:	Mathematical statistics > Data processing. Python (Computer program language) Regression analysis. Regression Analysis Statistique mathématique > Informatique. Python (Langage de programmation) Analyse de régression. Mathematical statistics > Data processing Regression analysis
Online Access:	Connect to this streaming video

Similar Items

Linear and polynomial regression in Python.
Published: (2021)

Linear and nonlinear regression in Python.
Published: (2021)

Nonlinear regression in Python.
Published: (2021)

Learn about random forest in Python with data from the Adult Census Income dataset (1996) /
by: Shi, Feng
Published: (2019)

Statistics essentials with Python.
Published: (2018)

Curve fit with Excel and Python.
Published: (2021)

Learn about cross validation in Python with data from the Adult Census Income dataset (1996) /
by: Shi, Feng, active 2019
Published: (2019)

Implementing KNN algorithm in R.
Published: (2019)

Implementing linear regression algorithm in R.
Published: (2019)

Estimating the LATE using regression analysis.
Published: (2020)

Learn about geographically weighted models in Python using Airbnb data in Berlin residential districts (2018) /
by: Wolf, Levi John
Published: (2019)

Time series analysis.
Published: (2018)

Learn about PageRank in Python with data from the Florentine Family dataset (1994) /
by: Shi, Feng, active 2019
Published: (2019)

Applied univariate, bivariate, and multivariate statistics using Python : a beginner's guide to advanced data analysis /
by: Denis, Daniel J., 1974-
Published: (2021)

Analyzing structured data.
Published: (2018)

Introduction to computational models with Python /
by: Garrido, Jose M.
Published: (2016)

Basic statistics and regression for machine learning in Python /
Published: (2021)

Mathematical algorithms for linear regression /
by: Späth, Helmuth
Published: (1992)

Quantile regression /
by: Koenker, Roger, 1947-
Published: (2005)

Machine learning with R : introduction and regression.
Published: (2018)

SN Video coding and web development.
Published: (2020)

An introduction to scientific computing with Matlab and Python tutorials /
by: Xu, Sheng, 1973-
Published: (2022)

Primitive data types in Python.
Published: (2019)

Time Series Analysis with Python Cookbook Practical Recipes for the Complete Time Series Workflow, from Modern Data Engineering to Advanced Forecasting and Anomaly Detection.
by: Atwan, Tarek A.
Published: (2026)

Logistic regression : practice.
Published: (2018)

Foundations of statistics for data scientists : with R and Python /
by: Agresti, Alan, et al.
Published: (2022)

Linear regression : practice.
Published: (2018)

Linear and polynomial regression in MATLAB.
Published: (2021)

Mathematical optimization with Python.
Published: (2021)

Introduction to engineering and scientific computing with Python /
by: Clough, David E., et al.
Published: (2023)

Introduction to computational models with Python /
by: Garrido, José M.
Published: (2016)

Python programming and numerical methods : a guide for engineers and scientists /
by: Bayen, Alexandre M., et al.
Published: (2021)

Data analytics for finance using Python /
by: Untwal, Nitin Jaglal, et al.
Published: (2025)

Nonlinear digital filtering with Python : an introduction /
by: Pearson, Ronald K., 1952-
Published: (2015)

Outliers in regression.
Published: (2016)

Statistics Using Python.
by: Campesato, Oswald
Published: (2023)

PYTHON FOR MATHEMATICS.
by: Knight, Vincent
Published: (2025)

Hierarchical linear modeling.
Published: (2005)

Small sample modelling based on deep and broad forest regression : theory and industrial application /
by: Yu, Wen, et al.
Published: (2025)

Handbook of regression modeling in people analytics : with examples in R and Python /
by: McNulty, Keith
Published: (2021)