Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
Table of Contents (22 chapters)
1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

accumulated hazard function 369

Aikake Information Criterion (AIC) 278

alternative hypothesis 62

Anaconda 4

URL 4

Anderson-Darling test 91-96

ANOVA tests 117, 118

versus pairwise tests 117

ARIMA models 296-299

fitting 299-302

forecasting with 302, 303

AR(p) end-to-end example 277

building 280, 281

forecast, building 283

forecast, testing 281, 282

order of AR(p), selecting 278, 279

visual inspection 277

autocorrelation 253-257

structure 253

autocorrelation function (ACF) 165, 322

autoregressive 98

autoregressive (AR) models

AR(1) model 273-275

AR(2) model 275, 276

AR(p) end-to-end example 277

AR(p) model 272

order p, identifying using PACF 276

autoregressive integrated moving average (ARIMA) 252, 323

autoregressive...